v11.2.0

Beautify restart

.gitignore

@@ -17,3 +17,4 @@ compile_commands.json
 CTestTestfile.cmake
 _deps
 code/edgeAI.code-workspace
+.DS_Store

FeatureRequest.md

@@ -11,6 +11,15 @@
 
 ____
 
+#### #29 Add favicon and use the hostname for the website
+
+* https://github.com/jomjol/AI-on-the-edge-device/issues/927
+
+#### #28 Improved error handling for ROIs
+
+* In case a ROI is out of the image, there is no error message, but a non sense image is used
+* Implement a error message for wrong configuratioin of ROI
+
 #### #27 Use Homie Spec for Mqtt binding
 
 * Use the standardized Home Protocol for the Mqtt binding 
@@ -55,7 +64,8 @@ ____
 #### #20 Deep sleep and push mode
 
 * Let the device be normally in deep sleep state, and wake it up periodically to collect data and push it via MQTT or HTTP post.
-
+* Support ESP-NOW to reduce the overhead of connecting to wifi and mqtt 
+* the above should enable battery powered applications
   
 
 #### #19 Extended log informations

README.md

@@ -40,7 +40,22 @@ In other cases you can contact the developer via email: <img src="https://raw.gi
 
 ------
 
-##### 11.0.1 - Intermediate Digits
+##### 11.2.0 - Intermediate Digits (2022-08-28)
+
+- Updated Tensorflow / TFlite to newest tflite (version as of 2022-07-27)
+- Updated analog neural network file (`ana-cont_11.3.0_s2.tflite` - default, `ana-class100_0120_s1_q.tflite`)
+- Updated digital neural network file (`dig-cont_0570_s3.tflite` - default, `dig-class100_0120_s2_q.tflite`)
+
+- Added automated filtering of tflite-file in the graphical configuration (thanks to @**[caco3](https://github.com/caco3)**)
+- Updated consistency algorithm & test cases
+- HTML: added favicon and system name, Improved reboot dialog  (thanks to @**[caco3](https://github.com/caco3)**)
+
+##### 11.1.1 - Intermediate Digits (2022-08-22)
+
+- New and improved consistency check (especially with analog and digital counters mixed)
+- Bug Fix: digital counter algorithm
+
+##### 11.0.1 - Intermediate Digits (2022-08-18)
 
 - **NEW v11.0.1**: Bug Fix InfluxDB configuration (only update of html.zip necessary)
 

code/components/esp-nn/CMakeLists.txt

@@ -5,7 +5,9 @@ set(c_srcs
     "src/basic_math/esp_nn_add_ansi.c"
     "src/basic_math/esp_nn_mul_ansi.c"
     "src/convolution/esp_nn_conv_ansi.c"
+    "src/convolution/esp_nn_conv_opt.c"
     "src/convolution/esp_nn_depthwise_conv_ansi.c"
+    "src/convolution/esp_nn_depthwise_conv_opt.c"
     "src/fully_connected/esp_nn_fully_connected_ansi.c"
     "src/softmax/esp_nn_softmax_ansi.c"
     "src/softmax/esp_nn_softmax_opt.c"
@@ -23,7 +25,7 @@ if(CONFIG_IDF_TARGET_ESP32S3)
         "src/convolution/esp_nn_conv_esp32s3.c"
         "src/convolution/esp_nn_depthwise_conv_s8_esp32s3.c"
         "src/convolution/esp_nn_conv_s16_mult8_esp32s3.S"
-        "src/convolution/esp_nn_conv_s16_mult8_1x1_esp32s3.S"
+        "src/convolution/esp_nn_conv_s8_mult8_1x1_esp32s3.S"
         "src/convolution/esp_nn_conv_s16_mult4_1x1_esp32s3.S"
         "src/convolution/esp_nn_depthwise_conv_s8_mult1_3x3_padded_esp32s3.S"
         "src/convolution/esp_nn_depthwise_conv_s16_mult1_esp32s3.S"

code/components/esp-nn/Kconfig.projbuild

@@ -6,8 +6,8 @@ choice NN_OPTIMIZATIONS
    help
       Use ANSI-C versions for verification and debug purpose.
       Optimisations are automatically picked up for a chipset.
-      For ESP32-S3, assembly Optimisations are selected.
-      For ESP32, just the ANSI C versions are selected for now.
+      For ESP32-S3, assembly optimisations are selected.
+      For other platforms(viz., ESP32, ESP32-C3), generic optimisations are used.
 
 config NN_ANSI_C
    bool "ANSI C"
@@ -17,8 +17,8 @@ config NN_OPTIMIZED
    bool "Optimized versions"
    help
       Optimisations are automatically picked up for a chipset.
-      For ESP32-S3, assembly Optimisations are selected.
-      For ESP32, just the ANSI C versions are selected for now.
+      For ESP32-S3, assembly optimisations are selected.
+      For other platforms(viz., ESP32, ESP32-C3), generic optimisations are used.
 endchoice
 
 config NN_OPTIMIZATIONS

code/components/esp-nn/README.md

@@ -7,7 +7,8 @@ The library contains optimised NN (Neural Network) functions for various Espress
 
 * Supported ESP chipsets include:
    * ESP32-S3 (Assembly versions optimised to benefit from vector instructions of ESP32-S3)
-   * ESP32 (ANSI C versions)
+   * ESP32 (Generic optimisations)
+   * ESP32-C3 (Generic optimisations)
 
 ## Performance
 
@@ -39,8 +40,8 @@ The library contains optimised NN (Neural Network) functions for various Espress
      * Optimized versions
      * ANSI C
 
-  * Default selection is for `Optimized versions`. For ESP32-S3, assembly versions are automatically selected, whereas for ESP32,  ANSI-C versions are selected by default.
-  * For debugging purposes, you may want to select `ANSI C`
+  * Default selection is for `Optimized versions`. For ESP32-S3, assembly versions are automatically selected, whereas for other chipsets (viz., ESP32, ESP32-C3), generic optimisations are selected.
+  * For debugging purposes, you may want to select `ANSI C` reference versions.
 
 
 ## Contributing

code/components/esp-nn/include/esp_nn.h

@@ -15,6 +15,7 @@
 #pragma once
 
 #if defined(CONFIG_NN_OPTIMIZED)
+// select apt optimisations
 #ifdef CONFIG_IDF_TARGET_ESP32S3
 #define ARCH_ESP32_S3 1
 #endif
@@ -31,12 +32,11 @@ extern "C" {
 #include "esp_nn_ansi_headers.h"
 
 #if defined(CONFIG_NN_OPTIMIZED)
-#ifdef ARCH_ESP32_S3
+#if defined(ARCH_ESP32_S3)
 #include "esp_nn_esp32s3.h"
-#endif
-#ifdef ARCH_ESP32
-#include "esp_nn_esp32.h"
-#endif
+#else // for other platforms use generic optimisations
+#include "esp_nn_generic_opt.h"
+#endif // #if defined(ARCH_ESP32_S3)
 #else
 #include "esp_nn_ansi_c.h"
 #endif

code/components/esp-nn/include/esp_nn_ansi_c.h

@@ -19,6 +19,7 @@
 
 #pragma once
 
+#include "esp_nn_defs.h"
 #include "esp_nn_ansi_headers.h"
 
 #define esp_nn_add_elementwise_s8 esp_nn_add_elementwise_s8_ansi

code/components/esp-nn/include/esp_nn_ansi_headers.h

@@ -18,8 +18,7 @@
  * @file        Header definitions to include for esp_nn reference functions
  */
 
-#include <stdint.h>
-
+#include "esp_nn_defs.h"
 /************************** Basic math functions ****************************/
 
 /**
@@ -81,28 +80,15 @@ void esp_nn_mul_elementwise_s8_ansi(const int8_t *input1_data,
  *              optimization notes: Though input_offset is int32 type,
  *              offset values are contained in 8 bits [-128, 127]
  */
-void esp_nn_depthwise_conv_s8_ansi(const int8_t *input_data,
-                                   const uint16_t input_wd,
-                                   const uint16_t input_ht,
-                                   const uint16_t channels,
-                                   const int32_t input_offset,
-                                   const uint16_t pad_wd,
-                                   const uint16_t pad_ht,
-                                   const uint16_t stride_wd,
-                                   const uint16_t stride_ht,
-                                   const uint16_t ch_mult,
+void esp_nn_depthwise_conv_s8_ansi(const data_dims_t *input_dims,
+                                   const int8_t *input_data,
+                                   const data_dims_t *filter_dims,
                                    const int8_t *filter_data,
-                                   const uint16_t filter_wd,
-                                   const uint16_t filter_ht,
                                    const int32_t *bias,
+                                   const data_dims_t *output_dims,
                                    int8_t *out_data,
-                                   const uint16_t out_wd,
-                                   const uint16_t out_ht,
-                                   const int32_t out_offset,
-                                   const int32_t *out_shift,
-                                   const int32_t *out_mult,
-                                   const int32_t activation_min,
-                                   const int32_t activation_max);
+                                   const dw_conv_params_t *conv_params,
+                                   const quant_data_t *quant_data);
 
 /**
  * @brief       2d-convolution channelwise
@@ -112,43 +98,26 @@ void esp_nn_depthwise_conv_s8_ansi(const int8_t *input_data,
  *              inputs type: int8_t, output: int8_t
  *              input offsets: although int32_t, they are contained in 8 bits [-128, 127]
  */
-void esp_nn_conv_s8_ansi(const int8_t *input_data,
-                         const uint16_t input_wd,
-                         const uint16_t input_ht,
-                         const uint16_t in_channels,
-                         const int32_t input_offset,
-                         const uint16_t pad_wd,
-                         const uint16_t pad_ht,
-                         const uint16_t stride_wd,
-                         const uint16_t stride_ht,
+void esp_nn_conv_s8_ansi(const data_dims_t *input_dims,
+                         const int8_t *input_data,
+                         const data_dims_t *filter_dims,
                          const int8_t *filter_data,
-                         const uint16_t filter_wd,
-                         const uint16_t filter_ht,
                          const int32_t *bias,
+                         const data_dims_t *output_dims,
                          int8_t *out_data,
-                         const uint16_t out_wd,
-                         const uint16_t out_ht,
-                         const uint16_t out_channels,
-                         const int32_t out_offset,
-                         const int32_t *out_shift,
-                         const int32_t *out_mult,
-                         const int32_t activation_min,
-                         const int32_t activation_max);
+                         const conv_params_t *conv_params,
+                         const quant_data_t *quant_data);
 
-int esp_nn_get_conv_scratch_size_ansi(const uint16_t input_wd,
-                                      const uint16_t input_ht,
-                                      const uint16_t in_ch,
-                                      const uint16_t out_ch,
-                                      const uint16_t filter_wd,
-                                      const uint16_t filter_ht);
+int esp_nn_get_conv_scratch_size_ansi(const data_dims_t *input_dims,
+                                      const data_dims_t *filter_dims,
+                                      const data_dims_t *output_dims,
+                                      const conv_params_t *conv_params);
 void esp_nn_set_conv_scratch_buf_ansi(const void *buf);
 
-int esp_nn_get_depthwise_conv_scratch_size_ansi(const uint16_t input_wd,
-                                                const uint16_t input_ht,
-                                                const uint16_t channels,
-                                                const uint16_t ch_mult,
-                                                const uint16_t filter_wd,
-                                                const uint16_t filter_ht);
+int esp_nn_get_depthwise_conv_scratch_size_ansi(const data_dims_t *input_dims,
+                                                const data_dims_t *filter_dims,
+                                                const data_dims_t *output_dims,
+                                                const dw_conv_params_t *conv_params);
 void esp_nn_set_depthwise_conv_scratch_buf_ansi(const void *buf);
 
 /************************** Activation functions *****************************/
@@ -252,9 +221,6 @@ int32_t esp_nn_get_softmax_scratch_size_opt(const int32_t width, const int32_t h
  */
 void esp_nn_set_softmax_scratch_buf_ansi(void *buffer);
 
-/* ANSI C function to be hooked up when optimised version needed */
-void esp_nn_set_softmax_scratch_buf_opt(void *buffer);
-
 /**
  * @brief       reference softmax function
  *
@@ -268,6 +234,66 @@ void esp_nn_softmax_s8_ansi(const int8_t *input_data,
                             const int32_t diff_min,
                             int8_t *output_data);
 
+
+//////////////////////////// Generic optimisations /////////////////////////////
+
+/************************** Convolution functions *****************************/
+
+/**
+ * @brief       2d-convolution channelwise optimized version
+ *
+ * @note        operation: result += (input + offset) * filter
+ *
+ *              inputs type: int8_t, output: int8_t
+ *              input offsets: although int32_t, they are contained in 8 bits [-128, 127]
+ */
+void esp_nn_conv_s8_opt(const data_dims_t *input_dims,
+                        const int8_t *input_data,
+                        const data_dims_t *filter_dims,
+                        const int8_t *filter_data,
+                        const int32_t *bias,
+                        const data_dims_t *output_dims,
+                        int8_t *out_data,
+                        const conv_params_t *conv_params,
+                        const quant_data_t *quant_data);
+
+/**
+ * @brief       depthwise convolution per channel optimized version
+ *
+ * @note        inputs type: int8_t, output: int8_t
+ *              Version used in tflite is per channel.
+ *              This version follows the same footsprints.
+ *              Meaning, it has per out_channel shift and multiplier for
+ *              requantization
+ *
+ *              optimization notes: Though input_offset is int32 type,
+ *              offset values are contained in 8 bits [-128, 127]
+ */
+void esp_nn_depthwise_conv_s8_opt(const data_dims_t *input_dims,
+                                  const int8_t *input_data,
+                                  const data_dims_t *filter_dims,
+                                  const int8_t *filter_data,
+                                  const int32_t *bias,
+                                  const data_dims_t *output_dims,
+                                  int8_t *out_data,
+                                  const dw_conv_params_t *conv_params,
+                                  const quant_data_t *quant_data);
+
+int esp_nn_get_conv_scratch_size_opt(const data_dims_t *input_dims,
+                                     const data_dims_t *filter_dims,
+                                     const data_dims_t *output_dims,
+                                     const conv_params_t *conv_params);
+void esp_nn_set_conv_scratch_buf_opt(const void *buf);
+
+int esp_nn_get_depthwise_conv_scratch_size_opt(const data_dims_t *input_dims,
+                                               const data_dims_t *filter_dims,
+                                               const data_dims_t *output_dims,
+                                               const dw_conv_params_t *conv_params);
+void esp_nn_set_depthwise_conv_scratch_buf_opt(const void *buf);
+
+/* ANSI C function to be hooked up when optimised version needed */
+void esp_nn_set_softmax_scratch_buf_opt(void *buffer);
+
 /**
  * @brief       optimised version of softmax function
  *

code/components/esp-nn/include/esp_nn_defs.h

@@ -0,0 +1,83 @@
+// Copyright 2022 Espressif Systems (Shanghai) PTE LTD
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+
+#include <stdint.h>
+
+/**
+ * @brief structure to club data dims
+ * this structure can be used for input, output and filter
+ */
+typedef struct data_dims {
+    int32_t width;
+    int32_t height;
+    int32_t channels;
+
+    int32_t extra; // can be used as batch or any other param
+} data_dims_t;
+
+/**
+ * @brief 2d data structure (width, height)
+ *
+ */
+typedef struct data_2d {
+    int32_t width;
+    int32_t height;
+} data_2d_t;
+
+/**
+ * @brief min/max activation
+ */
+typedef struct act_params {
+    int32_t min;
+    int32_t max;
+} act_params_t;
+
+/**
+ * @brief per channel quant data
+ *
+ * @note number of shift and mult elements are equal to output channels
+ */
+typedef struct quant_data {
+    int32_t *shift;
+    int32_t *mult;
+} quant_data_t;
+
+/**
+ * @brief params specific to convolution 2d
+ *
+ */
+typedef struct conv_params {
+    int32_t in_offset;
+    int32_t out_offset;
+    data_2d_t stride;
+    data_2d_t padding;
+    data_2d_t dilation;
+    act_params_t activation;
+} conv_params_t;
+
+/**
+ * @brief params specific to depthwise convolution 2d
+ *
+ */
+typedef struct dw_conv_params {
+    int32_t in_offset;
+    int32_t out_offset;
+    int32_t ch_mult; // channel multiplier. (in_ch * ch_mult = out_ch)
+    data_2d_t stride;
+    data_2d_t padding;
+    data_2d_t dilation;
+    act_params_t activation;
+} dw_conv_params_t;

code/components/esp-nn/include/esp_nn_esp32s3.h

@@ -19,7 +19,7 @@
 
 #pragma once
 
-#include <stdint.h>
+#include "esp_nn_defs.h"
 #include "esp_nn_ansi_headers.h"
 
 /************************** Basic math functions *****************************/
@@ -85,28 +85,15 @@ void esp_nn_mul_elementwise_s8_esp32s3(const int8_t *input1_data,
  *              optimization notes: Though input_offset is int32 type,
  *              offset values are contained in 8 bits [-128, 127]
  */
-void esp_nn_depthwise_conv_s8_esp32s3(const int8_t *input_data,
-                                      const uint16_t input_wd,
-                                      const uint16_t input_ht,
-                                      const uint16_t channels,
-                                      const int32_t input_offset,
-                                      const uint16_t pad_wd,
-                                      const uint16_t pad_ht,
-                                      const uint16_t stride_wd,
-                                      const uint16_t stride_ht,
-                                      const uint16_t ch_mult,
+void esp_nn_depthwise_conv_s8_esp32s3(const data_dims_t *input_dims,
+                                      const int8_t *input_data,
+                                      const data_dims_t *filter_dims,
                                       const int8_t *filter_data,
-                                      const uint16_t filter_wd,
-                                      const uint16_t filter_ht,
                                       const int32_t *bias,
-                                      int8_t *out_data,
-                                      const uint16_t out_wd,
-                                      const uint16_t out_ht,
-                                      const int32_t out_offset,
-                                      const int32_t *out_shift,
-                                      const int32_t *out_mult,
-                                      const int32_t activation_min,
-                                      const int32_t activation_max);
+                                      const data_dims_t *output_dims,
+                                      int8_t *output_data,
+                                      const dw_conv_params_t *conv_params,
+                                      const quant_data_t *quant_data);
 
 /**
  * @brief       2d - convolution channelwise
@@ -116,43 +103,26 @@ void esp_nn_depthwise_conv_s8_esp32s3(const int8_t *input_data,
  *              inputs type: int8_t, output: int8_t
  *              input offsets: although int32_t, they are contained in 8 bits [-128, 127]
  */
-void esp_nn_conv_s8_esp32s3(const int8_t *input_data,
-                            const uint16_t input_wd,
-                            const uint16_t input_ht,
-                            const uint16_t in_channels,
-                            const int32_t input_offset,
-                            const uint16_t pad_wd,
-                            const uint16_t pad_ht,
-                            const uint16_t stride_wd,
-                            const uint16_t stride_ht,
+void esp_nn_conv_s8_esp32s3(const data_dims_t *input_dims,
+                            const int8_t *input_data,
+                            const data_dims_t *filter_dims,
                             const int8_t *filter_data,
-                            const uint16_t filter_wd,
-                            const uint16_t filter_ht,
                             const int32_t *bias,
-                            int8_t *out_data,
-                            const uint16_t out_wd,
-                            const uint16_t out_ht,
-                            const uint16_t out_channels,
-                            const int32_t out_offset,
-                            const int32_t *out_shift,
-                            const int32_t *out_mult,
-                            const int32_t activation_min,
-                            const int32_t activation_max);
+                            const data_dims_t *output_dims,
+                            int8_t *output_data,
+                            const conv_params_t *conv_params,
+                            const quant_data_t *quant_data);
 
-int esp_nn_get_conv_scratch_size_esp32s3(const uint16_t input_wd,
-                                         const uint16_t input_ht,
-                                         const uint16_t in_ch,
-                                         const uint16_t out_ch,
-                                         const uint16_t filter_wd,
-                                         const uint16_t filter_ht);
+int esp_nn_get_conv_scratch_size_esp32s3(const data_dims_t *input_dims,
+                                         const data_dims_t *filter_dims,
+                                         const data_dims_t *output_dims,
+                                         const conv_params_t *conv_params);
 void esp_nn_set_conv_scratch_buf_esp32s3(const void *buf);
 
-int esp_nn_get_depthwise_conv_scratch_size_esp32s3(const uint16_t input_wd,
-                                                   const uint16_t input_ht,
-                                                   const uint16_t channels,
-                                                   const uint16_t ch_mult,
-                                                   const uint16_t filter_wd,
-                                                   const uint16_t filter_ht);
+int esp_nn_get_depthwise_conv_scratch_size_esp32s3(const data_dims_t *input_dims,
+                                                   const data_dims_t *filter_dims,
+                                                   const data_dims_t *output_dims,
+                                                   const dw_conv_params_t *conv_params);
 void esp_nn_set_depthwise_conv_scratch_buf_esp32s3(const void *buf);
 
 /************************** Pooling functions *****************************/

code/components/esp-nn/include/esp_nn_generic_opt.h

@@ -13,28 +13,27 @@
 // limitations under the License.
 
 /**
- * @file        Header definitions to include for esp_nn optimized functions for
- *              the ESP32 platform.
- *              We are hooking up just the C versions for now.
- *              The file hence is exactly same as `esp_nn_ansi_c.h`
+ * @file        Header definitions to include for esp_nn generic optimisations
+ *              For functions which not having optimisations, _ansi versions are picked.
  */
 
 #pragma once
 
+#include "esp_nn_defs.h"
 #include "esp_nn_ansi_headers.h"
 
 #define esp_nn_add_elementwise_s8 esp_nn_add_elementwise_s8_ansi
 #define esp_nn_mul_elementwise_s8 esp_nn_mul_elementwise_s8_ansi
 
-#define esp_nn_depthwise_conv_s8 esp_nn_depthwise_conv_s8_ansi
+#define esp_nn_depthwise_conv_s8 esp_nn_depthwise_conv_s8_opt
 
-#define esp_nn_conv_s8 esp_nn_conv_s8_ansi
+#define esp_nn_conv_s8 esp_nn_conv_s8_opt
 
-#define esp_nn_get_conv_scratch_size esp_nn_get_conv_scratch_size_ansi
-#define esp_nn_set_conv_scratch_buf esp_nn_set_conv_scratch_buf_ansi
+#define esp_nn_get_conv_scratch_size esp_nn_get_conv_scratch_size_opt
+#define esp_nn_set_conv_scratch_buf esp_nn_set_conv_scratch_buf_opt
 
-#define esp_nn_get_depthwise_conv_scratch_size esp_nn_get_depthwise_conv_scratch_size_ansi
-#define esp_nn_set_depthwise_conv_scratch_buf esp_nn_set_depthwise_conv_scratch_buf_ansi
+#define esp_nn_get_depthwise_conv_scratch_size esp_nn_get_depthwise_conv_scratch_size_opt
+#define esp_nn_set_depthwise_conv_scratch_buf esp_nn_set_depthwise_conv_scratch_buf_opt
 
 #define esp_nn_relu6_s8 esp_nn_relu6_s8_ansi
 

code/components/esp-nn/src/common/common_functions.h

@@ -41,15 +41,39 @@
 
 __NN_FORCE_INLINE__ int32_t esp_nn_clz32(uint32_t in)
 {
+#if CONFIG_IDF_TARGET_ARCH_XTENSA
     __asm__ volatile("nsau %0, %0" : "+r" (in));
     return in;
-}
-
-__NN_FORCE_INLINE__ int32_t esp_nn_pick_sat_high32_of64(int64_t val64)
-{
-    int32_t sign = (int32_t) (val64 >> 63);
-    int32_t to_add = sign & ((1ul << 31) - 1);
-    return (int32_t) ((int64_t) (val64 + to_add) >> 31);
+#elif defined(__GNUC__)
+    return __builtin_clz(in);
+#else
+    int32_t count = 32;
+    uint32_t x = in, y = in >> 16;
+    if (y != 0) {
+        count -= 16;
+        x = y;
+    }
+    y = x >> 8;
+    if (y != 0) {
+        count -= 8;
+        x = y;
+    }
+    y = x >> 4;
+    if (y != 0) {
+        count -= 4;
+        x = y;
+    }
+    y = x >> 2;
+    if (y != 0) {
+        count -= 2;
+        x = y;
+    }
+    y = x >> 1;
+    if (y != 0) {
+        return count - 2;
+    }
+    return count - x;
+#endif
 }
 
 /**
@@ -57,8 +81,19 @@ __NN_FORCE_INLINE__ int32_t esp_nn_pick_sat_high32_of64(int64_t val64)
  */
 __NN_FORCE_INLINE__ int32_t esp_nn_saturate8(int32_t in)
 {
+#if CONFIG_IDF_TARGET_ARCH_XTENSA
     __asm__ volatile("clamps %0, %0, 7" : "+a"(in));
     return in;
+#else
+    return max(INT8_MIN, min(in, INT8_MAX));
+#endif
+}
+
+__NN_FORCE_INLINE__ int32_t esp_nn_pick_sat_high32_of64(int64_t val64)
+{
+    int32_t sign = (int32_t) (val64 >> 63);
+    int32_t to_add = sign & ((1ul << 31) - 1);
+    return (int32_t) ((int64_t) (val64 + to_add) >> 31);
 }
 
 __NN_FORCE_INLINE__ int32_t esp_nn_sat_round_doubling_high_mul(int32_t in0, int32_t in1)
@@ -144,7 +179,7 @@ static void esp_nn_aligned_s8_pad_with_value(const int8_t *src, int8_t *dst,
                                              const uint16_t pad_ht)
 {
     /* memset with pad_val */
-    memset(dst, pad_val, ((input_wd + 2 * pad_wd) * (input_ht + 2 * pad_ht)) * channels * 2);
+    memset(dst, pad_val, ((input_wd + 2 * pad_wd) * (input_ht + 2 * pad_ht)) * channels);
     dst += (pad_wd + input_wd + pad_wd) * channels;
 
     for (int i = 0; i < input_ht; i++) {
@@ -156,7 +191,6 @@ static void esp_nn_aligned_s8_pad_with_value(const int8_t *src, int8_t *dst,
     }
 }
 
-#if 0
 static void esp_nn_aligned_s8_pad_end_with_value(const int8_t *src, int8_t *dst,
                                                  const uint16_t input_wd,
                                                  const uint16_t input_ht,
@@ -169,13 +203,16 @@ static void esp_nn_aligned_s8_pad_end_with_value(const int8_t *src, int8_t *dst,
         for (int j = 0; j < input_wd * channels; j++) {
             *dst++ = *src++;
         }
-        memset(dst, pad_val, pad_wd * channels);
-        dst += pad_wd * channels;
+        if (pad_wd) {
+            memset(dst, pad_val, pad_wd * channels);
+            dst += pad_wd * channels;
+        }
     }
     /* pad end `pad_ht` lines at end */
-    memset(dst, pad_val, (input_wd + pad_wd) * pad_ht * channels);
+    if (pad_ht) {
+        memset(dst, pad_val, (input_wd + pad_wd) * pad_ht * channels);
+    }
 }
-#endif
 
 /**
  * @brief       convert 8 bit input data to 16 bit

code/components/esp-nn/src/convolution/esp_nn_conv_ansi.c

@@ -12,16 +12,14 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 
-#include <stdint.h>
+#include <esp_nn_defs.h>
 
 #include <common_functions.h>
 
-int esp_nn_get_conv_scratch_size_ansi(const uint16_t input_wd,
-                                      const uint16_t input_ht,
-                                      const uint16_t in_ch,
-                                      const uint16_t out_ch,
-                                      const uint16_t filter_wd,
-                                      const uint16_t filter_ht)
+int esp_nn_get_conv_scratch_size_ansi(const data_dims_t *input_dims,
+                                      const data_dims_t *filter_dims,
+                                      const data_dims_t *output_dims,
+                                      const conv_params_t *conv_params)
 {
     return 0;
 }
@@ -108,29 +106,35 @@ void esp_nn_conv_u8_ansi(const uint8_t *input_data,
  * Assumption 2: Pointers are valid
  * Assumption 3: dialation width = 1
  */
-void esp_nn_conv_s8_ansi(const int8_t *input_data,
-                         const uint16_t input_wd,
-                         const uint16_t input_ht,
-                         const uint16_t in_channels,
-                         const int32_t input_offset,
-                         const uint16_t pad_wd,
-                         const uint16_t pad_ht,
-                         const uint16_t stride_wd,
-                         const uint16_t stride_ht,
+void esp_nn_conv_s8_ansi(const data_dims_t *input_dims,
+                         const int8_t *input_data,
+                         const data_dims_t *filter_dims,
                          const int8_t *filter_data,
-                         const uint16_t filter_wd,
-                         const uint16_t filter_ht,
                          const int32_t *bias,
+                         const data_dims_t *output_dims,
                          int8_t *out_data,
-                         const uint16_t out_wd,
-                         const uint16_t out_ht,
-                         const uint16_t out_channels,
-                         const int32_t out_offset,
-                         const int32_t *out_shift,
-                         const int32_t *out_mult,
-                         const int32_t activation_min,
-                         const int32_t activation_max)
+                         const conv_params_t *conv_params,
+                         const quant_data_t *quant_data)
 {
+    const uint16_t input_wd = input_dims->width;
+    const uint16_t input_ht = input_dims->height;
+    const uint16_t in_channels = input_dims->channels;
+    const int32_t input_offset = conv_params->in_offset;
+    const int32_t out_offset = conv_params->out_offset;
+    const uint16_t pad_wd = conv_params->padding.width;
+    const uint16_t pad_ht = conv_params->padding.height;
+    const uint16_t stride_wd = conv_params->stride.width;
+    const uint16_t stride_ht = conv_params->stride.height;
+    const uint16_t filter_wd = filter_dims->width;
+    const uint16_t filter_ht = filter_dims->height;
+    const uint16_t out_wd = output_dims->width;
+    const uint16_t out_ht = output_dims->height;
+    const uint16_t out_channels = output_dims->channels;
+    const int32_t *out_shift = quant_data->shift;
+    const int32_t *out_mult = quant_data->mult;
+    const int32_t activation_min = conv_params->activation.min;
+    const int32_t activation_max = conv_params->activation.max;
+
     int32_t out_ch_idx, out_y, out_x, in_ch_idx, filter_y_idx, filter_x_idx;
 
     for (out_y = 0; out_y < out_ht; out_y++) {

code/components/esp-nn/src/convolution/esp_nn_conv_esp32s3.c

@@ -12,30 +12,30 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 
-#include <stdint.h>
 #include <stdio.h>
+#include <esp_nn_defs.h>
 
 #include <common_functions.h>
 
 static int16_t *scratch_buffer = NULL;
 
-extern void esp_nn_conv_s16_mult8_1x1_esp32s3(const int8_t *input_data,
-                                              const uint16_t input_wd,
-                                              const uint16_t input_ht,
-                                              const uint16_t in_channels,
-                                              const int32_t input_offset,
-                                              const int16_t *filter_data,
-                                              const int32_t *bias,
-                                              int8_t *out_data,
-                                              const uint16_t out_wd,
-                                              const uint16_t out_ht,
-                                              const uint16_t out_channels,
-                                              const int32_t out_offset,
-                                              const int32_t *out_shift,
-                                              const int32_t *out_mult,
-                                              const int32_t activation_min,
-                                              const int32_t activation_max,
-                                              void *buffer /* scratch buffer */);
+extern void esp_nn_conv_s8_mult8_1x1_esp32s3(const int8_t *input_data,
+                                             const uint16_t input_wd,
+                                             const uint16_t input_ht,
+                                             const uint16_t in_channels,
+                                             const int32_t input_offset,
+                                             const int8_t *filter_aligned,
+                                             const int32_t *bias,
+                                             int8_t *out_data,
+                                             const uint16_t out_wd,
+                                             const uint16_t out_ht,
+                                             const uint16_t out_channels,
+                                             const int32_t out_offset,
+                                             const int32_t *out_shift,
+                                             const int32_t *out_mult,
+                                             const int32_t activation_min,
+                                             const int32_t activation_max,
+                                             void *buffer /* scratch buffer */);
 
 extern void esp_nn_conv_s16_mult4_1x1_esp32s3(const int16_t *input_data,
                                               const uint16_t input_wd,
@@ -81,34 +81,40 @@ extern void esp_nn_aligned_s8_to_s16_with_offset_esp32s3(const int8_t *src, int1
 
 extern void esp_nn_s8_to_s16_esp32s3(const int8_t *src, int16_t *dst, const int size);
 
-static void esp_nn_conv_s8_unrolled(const int8_t *input_data,
-                                    const uint16_t input_wd,
-                                    const uint16_t input_ht,
-                                    const uint16_t in_channels,
-                                    const int32_t input_offset,
-                                    const uint16_t pad_wd,
-                                    const uint16_t pad_ht,
-                                    const uint16_t stride_wd,
-                                    const uint16_t stride_ht,
+static void esp_nn_conv_s8_unrolled(const data_dims_t *input_dims,
+                                    const int8_t *input_data,
+                                    const data_dims_t *filter_dims,
                                     const int8_t *filter_data,
-                                    const uint16_t filter_wd,
-                                    const uint16_t filter_ht,
                                     const int32_t *bias,
+                                    const data_dims_t *output_dims,
                                     int8_t *out_data,
-                                    const uint16_t out_wd,
-                                    const uint16_t out_ht,
-                                    const uint16_t out_channels,
-                                    const int32_t out_offset,
-                                    const int32_t *out_shift,
-                                    const int32_t *out_mult,
-                                    const int32_t activation_min,
-                                    const int32_t activation_max)
+                                    const conv_params_t *conv_params,
+                                    const quant_data_t *quant_data)
 {
+    const uint16_t input_wd = input_dims->width;
+    const uint16_t input_ht = input_dims->height;
+    const uint16_t in_ch = input_dims->channels;
+    const int32_t input_offset = conv_params->in_offset;
+    const int32_t out_offset = conv_params->out_offset;
+    const uint16_t pad_wd = conv_params->padding.width;
+    const uint16_t pad_ht = conv_params->padding.height;
+    const uint16_t stride_wd = conv_params->stride.width;
+    const uint16_t stride_ht = conv_params->stride.height;
+    const uint16_t filter_wd = filter_dims->width;
+    const uint16_t filter_ht = filter_dims->height;
+    const uint16_t out_wd = output_dims->width;
+    const uint16_t out_ht = output_dims->height;
+    const uint16_t out_ch = output_dims->channels;
+    const int32_t *out_shift = quant_data->shift;
+    const int32_t *out_mult = quant_data->mult;
+    const int32_t activation_min = conv_params->activation.min;
+    const int32_t activation_max = conv_params->activation.max;
+
     int32_t out_ch_idx, out_y, out_x, in_ch_idx, filter_y_idx, filter_x_idx;
 
     for (out_y = 0; out_y < out_ht; out_y++) {
         for (out_x = 0; out_x < out_wd; out_x++) {
-            for (out_ch_idx = 0; out_ch_idx < out_channels; out_ch_idx++) {
+            for (out_ch_idx = 0; out_ch_idx < out_ch; out_ch_idx++) {
                 int32_t conv_out = 0;
 
                 const int32_t base_y = stride_ht * out_y - pad_ht;
@@ -124,10 +130,10 @@ static void esp_nn_conv_s8_unrolled(const int8_t *input_data,
                     for (filter_x_idx = filter_x_start; filter_x_idx < filter_x_end; filter_x_idx++) {
                         const int32_t in_row = base_y + filter_y_idx;
                         const int32_t in_col = base_x + filter_x_idx;
-                        int32_t input_base_offset = (in_row * input_wd + in_col) * in_channels;
-                        int32_t filter_base_offset = out_ch_idx * in_channels * filter_ht * filter_wd +
-                                                       (filter_y_idx * filter_wd + filter_x_idx) * in_channels;
-                        for (in_ch_idx = 0; in_ch_idx < in_channels; in_ch_idx++) {
+                        int32_t input_base_offset = (in_row * input_wd + in_col) * in_ch;
+                        int32_t filter_base_offset = out_ch_idx * in_ch * filter_ht * filter_wd +
+                                                       (filter_y_idx * filter_wd + filter_x_idx) * in_ch;
+                        for (in_ch_idx = 0; in_ch_idx < in_ch; in_ch_idx++) {
                             conv_out +=
                                 (input_data[input_base_offset + in_ch_idx] + input_offset) *
                                 filter_data[filter_base_offset + in_ch_idx];
@@ -332,18 +338,35 @@ static void esp_nn_conv_s8_pad_valid_ch3_3x3(const int8_t *input_data,
     }
 }
 
-int esp_nn_get_conv_scratch_size_esp32s3(const uint16_t input_wd,
-                                         const uint16_t input_ht,
-                                         const uint16_t in_ch,
-                                         const uint16_t out_ch,
-                                         const uint16_t filter_wd,
-                                         const uint16_t filter_ht)
+int esp_nn_get_conv_scratch_size_esp32s3(const data_dims_t *input_dims,
+                                         const data_dims_t *filter_dims,
+                                         const data_dims_t *output_dims,
+                                         const conv_params_t *conv_params)
 {
+    const uint16_t input_wd = input_dims->width;
+    const uint16_t input_ht = input_dims->height;
+    const uint16_t in_ch = input_dims->channels;
+    const uint16_t filter_wd = filter_dims->width;
+    const uint16_t filter_ht = filter_dims->height;
+    const uint16_t out_ch = output_dims->channels;
+    const uint16_t pad_wd = conv_params->padding.width;
+    const uint16_t pad_ht = conv_params->padding.height;
+    const uint16_t stride_wd = conv_params->stride.width;
+    const uint16_t stride_ht = conv_params->stride.height;
+
     int filter_size = filter_wd * filter_ht * in_ch * out_ch;
     int input_size = input_wd * input_ht * in_ch;
-    int transpose_buf_size = 8 * in_ch; /* to store intermediate data */
+
+    int transpose_buf_size = 2 * (8 * in_ch); /* to store intermediate data */
+    if (input_wd * input_ht < 8) {
+        transpose_buf_size = 0; // not using this for leftover
+    }
     int align_buf_size = 32; /* extra buffer for alignment */
-    return 2 * (filter_size + input_size +  transpose_buf_size) + align_buf_size;
+    if (in_ch % 8 == 0 && filter_wd == 1 && filter_ht == 1 &&
+            pad_wd == 0 && pad_ht == 0 && stride_wd == 1 && stride_ht == 1) {
+        return filter_size + transpose_buf_size + align_buf_size;
+    }
+    return 2 * (filter_size + input_size) +  transpose_buf_size + align_buf_size;
 }
 
 void esp_nn_set_conv_scratch_buf_esp32s3(void *buf)
@@ -351,29 +374,35 @@ void esp_nn_set_conv_scratch_buf_esp32s3(void *buf)
     scratch_buffer = (int16_t *) buf;
 }
 
-void esp_nn_conv_s8_esp32s3(const int8_t *input,
-                            const uint16_t input_wd,
-                            const uint16_t input_ht,
-                            const uint16_t channels,
-                            const int32_t input_offset,
-                            const uint16_t pad_wd,
-                            const uint16_t pad_ht,
-                            const uint16_t stride_wd,
-                            const uint16_t stride_ht,
+void esp_nn_conv_s8_esp32s3(const data_dims_t *input_dims,
+                            const int8_t *input,
+                            const data_dims_t *filter_dims,
                             const int8_t *filter_data,
-                            const uint16_t filter_wd,
-                            const uint16_t filter_ht,
                             const int32_t *bias,
+                            const data_dims_t *output_dims,
                             int8_t *out_data,
-                            const uint16_t out_wd,
-                            const uint16_t out_ht,
-                            const uint16_t out_channels,
-                            const int32_t out_offset,
-                            const int32_t *out_shift,
-                            const int32_t *out_mult,
-                            const int32_t activation_min,
-                            const int32_t activation_max)
+                            const conv_params_t *conv_params,
+                            const quant_data_t *quant_data)
 {
+    const uint16_t input_wd = input_dims->width;
+    const uint16_t input_ht = input_dims->height;
+    const uint16_t channels = input_dims->channels;
+    const int32_t input_offset = conv_params->in_offset;
+    const int32_t out_offset = conv_params->out_offset;
+    const uint16_t pad_wd = conv_params->padding.width;
+    const uint16_t pad_ht = conv_params->padding.height;
+    const uint16_t stride_wd = conv_params->stride.width;
+    const uint16_t stride_ht = conv_params->stride.height;
+    const uint16_t filter_wd = filter_dims->width;
+    const uint16_t filter_ht = filter_dims->height;
+    const uint16_t out_wd = output_dims->width;
+    const uint16_t out_ht = output_dims->height;
+    const uint16_t out_channels = output_dims->channels;
+    const int32_t *out_shift = quant_data->shift;
+    const int32_t *out_mult = quant_data->mult;
+    const int32_t activation_min = conv_params->activation.min;
+    const int32_t activation_max = conv_params->activation.max;
+
     int filter_size = filter_wd * filter_ht * channels * out_channels;
     int input_size = input_wd * input_ht * channels;
     int align_len = 16 - (filter_size & 15);
@@ -387,15 +416,16 @@ void esp_nn_conv_s8_esp32s3(const int8_t *input,
 
     if (channels % 8 == 0 && filter_wd == 1 && filter_ht == 1 &&
             pad_wd == 0 && pad_ht == 0 && stride_wd == 1 && stride_ht == 1) {
-        int scratch_offset = (int) (filter_data16 + filter_size);
+        int8_t *filter_aligned = (int8_t *) scratch_buffer;
+        int scratch_offset = (int) (filter_aligned + filter_size);
         void *scratch_buf = (void *) (scratch_offset + 16 - (scratch_offset & 15));
-        esp_nn_s8_to_s16_esp32s3(filter_data, filter_data16, filter_size);
-        esp_nn_conv_s16_mult8_1x1_esp32s3(
-            input, input_wd, input_ht, channels, input_offset, filter_data16,
+        memcpy(filter_aligned, filter_data, filter_size); // copy to aligned address
+        esp_nn_conv_s8_mult8_1x1_esp32s3(
+            input, input_wd, input_ht, channels, input_offset, filter_aligned,
             bias, out_data, out_wd, out_ht, out_channels, out_offset,
             out_shift, out_mult, activation_min, activation_max, scratch_buf);
     } else if (channels % 4 == 0 && filter_wd == 1 && filter_ht == 1 &&
-            (input_wd * input_ht) % 16 == 0 && /* TODO: remove this check */
+            (input_wd * input_ht) % 4 == 0 && /* TODO: remove this check */
             pad_wd == 0 && pad_ht == 0 && stride_wd == 1 && stride_ht == 1) {
         int scratch_offset = (int) (input_data16 + input_size);
         void *scratch_buf = (void *) (scratch_offset + 16 - (scratch_offset & 15));
@@ -427,10 +457,7 @@ void esp_nn_conv_s8_esp32s3(const int8_t *input,
         }
     } else {
         /* Basic unrolled version */
-        esp_nn_conv_s8_unrolled(input, input_wd, input_ht, channels, input_offset,
-                                pad_wd, pad_ht, stride_wd, stride_ht,
-                                filter_data, filter_wd, filter_ht, bias,
-                                out_data, out_wd, out_ht, out_channels, out_offset, out_shift,
-                                out_mult, activation_min, activation_max);
+        esp_nn_conv_s8_unrolled(input_dims, input, filter_dims, filter_data,
+                                bias, output_dims, out_data, conv_params, quant_data);
     }
 }

code/components/esp-nn/src/convolution/esp_nn_conv_opt.c

@@ -0,0 +1,179 @@
+// Copyright 2020-2021 Espressif Systems (Shanghai) PTE LTD
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include <esp_nn_defs.h>
+
+#include <common_functions.h>
+
+int esp_nn_get_conv_scratch_size_opt(const data_dims_t *input_dims,
+                                     const data_dims_t *filter_dims,
+                                     const data_dims_t *output_dims,
+                                     const conv_params_t *conv_params)
+{
+    return 0;
+}
+
+void esp_nn_set_conv_scratch_buf_opt(const void *buf)
+{
+
+}
+
+__attribute__ ((noinline))
+static void esp_nn_conv_s8_1x1(const data_dims_t *input_dims,
+                               const int8_t *input_data,
+                               const int8_t *filter_data,
+                               const int32_t *bias,
+                               const data_dims_t *output_dims,
+                               int8_t *out_data,
+                               const conv_params_t *conv_params,
+                               const quant_data_t *quant_data)
+{
+    const uint16_t input_wd = input_dims->width;
+    const uint16_t in_channels = input_dims->channels;
+    const int32_t input_offset = conv_params->in_offset;
+    const int32_t out_offset = conv_params->out_offset;
+    const uint16_t stride_wd = conv_params->stride.width;
+    const uint16_t stride_ht = conv_params->stride.height;
+    const uint16_t out_wd = output_dims->width;
+    const uint16_t out_ht = output_dims->height;
+    const uint16_t out_channels = output_dims->channels;
+    const int32_t activation_min = conv_params->activation.min;
+    const int32_t activation_max = conv_params->activation.max;
+
+    for (int32_t in_row = 0; in_row < out_ht * stride_ht; in_row += stride_ht) {
+        for (int32_t in_col = 0; in_col < out_wd * stride_wd; in_col += stride_wd) {
+            const int32_t *out_mult = quant_data->mult;
+            const int32_t *out_shift = quant_data->shift;
+            const int8_t *filter_ptr = filter_data;
+            const int8_t *input_base_ptr = input_data + (in_row * input_wd + in_col) * in_channels;
+            int32_t out_ch_idx = 0;
+            for (; out_ch_idx < out_channels; out_ch_idx++) {
+                int32_t conv_out = 0;
+
+                const int8_t *input_ptr = input_base_ptr;
+
+                int32_t in_ch_idx = 0;
+                for (; in_ch_idx < in_channels - 3; in_ch_idx += 4) {
+                    conv_out += (*input_ptr++ + input_offset) * *filter_ptr++;
+                    conv_out += (*input_ptr++ + input_offset) * *filter_ptr++;
+                    conv_out += (*input_ptr++ + input_offset) * *filter_ptr++;
+                    conv_out += (*input_ptr++ + input_offset) * *filter_ptr++;
+                }
+                for (; in_ch_idx < in_channels; in_ch_idx ++) {
+                    conv_out += (*input_ptr++ + input_offset) * *filter_ptr++;
+                }
+                if (bias) {
+                    conv_out += bias[out_ch_idx];
+                }
+                conv_out = esp_nn_multiply_by_quantized_mult_fast(conv_out, *out_mult++, *out_shift++);
+                conv_out += out_offset;
+                conv_out = max(conv_out, activation_min);
+                conv_out = min(conv_out, activation_max);
+                *out_data++ = (int8_t) conv_out;
+            }
+        }
+    }
+}
+
+/**
+ * Assumption 1: i/p channels == o/p channels
+ * Assumption 2: Pointers are valid
+ * Assumption 3: dialation width = 1
+ */
+void esp_nn_conv_s8_opt(const data_dims_t *input_dims,
+                        const int8_t *input_data,
+                        const data_dims_t *filter_dims,
+                        const int8_t *filter_data,
+                        const int32_t *bias,
+                        const data_dims_t *output_dims,
+                        int8_t *out_data,
+                        const conv_params_t *conv_params,
+                        const quant_data_t *quant_data)
+{
+    const uint16_t filter_wd = filter_dims->width;
+    const uint16_t filter_ht = filter_dims->height;
+
+    if (filter_wd == 1 && filter_ht == 1) {
+        esp_nn_conv_s8_1x1(input_dims, input_data, filter_data, bias,
+                           output_dims, out_data, conv_params, quant_data);
+        return;
+    }
+
+    const uint16_t input_wd = input_dims->width;
+    const uint16_t input_ht = input_dims->height;
+    const uint16_t in_channels = input_dims->channels;
+    const int32_t input_offset = conv_params->in_offset;
+    const int32_t out_offset = conv_params->out_offset;
+    const uint16_t pad_wd = conv_params->padding.width;
+    const uint16_t pad_ht = conv_params->padding.height;
+    const uint16_t stride_wd = conv_params->stride.width;
+    const uint16_t stride_ht = conv_params->stride.height;
+    const uint16_t out_wd = output_dims->width;
+    const uint16_t out_ht = output_dims->height;
+    const uint16_t out_channels = output_dims->channels;
+    const int32_t activation_min = conv_params->activation.min;
+    const int32_t activation_max = conv_params->activation.max;
+
+    int32_t out_ch_idx, out_y, out_x, filter_y_idx, filter_x_idx;
+
+    for (out_y = 0; out_y < out_ht; out_y++) {
+        for (out_x = 0; out_x < out_wd; out_x++) {
+            const int32_t *out_shift = quant_data->shift;
+            const int32_t *out_mult = quant_data->mult;
+            for (out_ch_idx = 0; out_ch_idx < out_channels; out_ch_idx++) {
+                int32_t conv_out = 0;
+
+                const int32_t base_y = stride_ht * out_y - pad_ht;
+                const int32_t base_x = stride_wd * out_x - pad_wd;
+
+                const int32_t filter_y_start = max(0, -base_y);
+                const int32_t filter_x_start = max(0, -base_x);
+
+                const int32_t filter_y_end = min(filter_ht, input_ht - base_y);
+                const int32_t filter_x_end = min(filter_wd, input_wd - base_x);
+
+                for (filter_y_idx = filter_y_start; filter_y_idx < filter_y_end; filter_y_idx++) {
+                    for (filter_x_idx = filter_x_start; filter_x_idx < filter_x_end; filter_x_idx++) {
+                        const int32_t in_row = base_y + filter_y_idx;
+                        const int32_t in_col = base_x + filter_x_idx;
+
+                        const int8_t *input_ptr = input_data +
+                                        (in_row * input_wd + in_col) * in_channels;
+                        const int8_t *filter_ptr = filter_data +
+                                        out_ch_idx * in_channels * filter_ht * filter_wd +
+                                        (filter_y_idx * filter_wd + filter_x_idx) * in_channels;
+                        int32_t in_ch_idx = 0;
+                        for (; in_ch_idx < in_channels - 3; in_ch_idx += 4) {
+                            conv_out += (*input_ptr++ + input_offset) * *filter_ptr++;
+                            conv_out += (*input_ptr++ + input_offset) * *filter_ptr++;
+                            conv_out += (*input_ptr++ + input_offset) * *filter_ptr++;
+                            conv_out += (*input_ptr++ + input_offset) * *filter_ptr++;
+                        }
+                        for (; in_ch_idx < in_channels; in_ch_idx ++) {
+                            conv_out += (*input_ptr++ + input_offset) * *filter_ptr++;
+                        }
+                    }
+                }
+                if (bias) {
+                    conv_out += bias[out_ch_idx];
+                }
+                conv_out = esp_nn_multiply_by_quantized_mult_fast(conv_out, *out_mult++, *out_shift++);
+                conv_out += out_offset;
+                conv_out = max(conv_out, activation_min);
+                conv_out = min(conv_out, activation_max);
+                *out_data++ = (int8_t) conv_out;
+            }
+        }
+    }
+}

code/components/esp-nn/src/convolution/esp_nn_depthwise_conv_ansi.c

@@ -12,16 +12,13 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 
-#include <stdint.h>
-
+#include <esp_nn_defs.h>
 #include <common_functions.h>
 
-int esp_nn_get_depthwise_conv_scratch_size_ansi(const uint16_t input_wd,
-                                                const uint16_t input_ht,
-                                                const uint16_t channels,
-                                                const uint16_t ch_mult,
-                                                const uint16_t filter_wd,
-                                                const uint16_t filter_ht)
+int esp_nn_get_depthwise_conv_scratch_size_ansi(const data_dims_t *input_dims,
+                                                const data_dims_t *filter_dims,
+                                                const data_dims_t *output_dims,
+                                                const dw_conv_params_t *conv_params)
 {
     return 0;
 }
@@ -31,29 +28,35 @@ void esp_nn_set_depthwise_conv_scratch_buf_ansi(const void *buf)
 
 }
 
-void esp_nn_depthwise_conv_s8_ansi(const int8_t *input_data,
-                                   const uint16_t input_wd,
-                                   const uint16_t input_ht,
-                                   const uint16_t channels,
-                                   const int32_t input_offset,
-                                   const uint16_t pad_wd,
-                                   const uint16_t pad_ht,
-                                   const uint16_t stride_wd,
-                                   const uint16_t stride_ht,
-                                   const uint16_t ch_mult,
+void esp_nn_depthwise_conv_s8_ansi(const data_dims_t *input_dims,
+                                   const int8_t *input_data,
+                                   const data_dims_t *filter_dims,
                                    const int8_t *filter_data,
-                                   const uint16_t filter_wd,
-                                   const uint16_t filter_ht,
                                    const int32_t *bias,
+                                   const data_dims_t *output_dims,
                                    int8_t *out_data,
-                                   const uint16_t out_wd,
-                                   const uint16_t out_ht,
-                                   const int32_t out_offset,
-                                   const int32_t *out_shift,
-                                   const int32_t *out_mult,
-                                   const int32_t activation_min,
-                                   const int32_t activation_max)
+                                   const dw_conv_params_t *conv_params,
+                                   const quant_data_t *quant_data)
 {
+    const uint16_t input_wd = input_dims->width;
+    const uint16_t input_ht = input_dims->height;
+    const uint16_t channels = input_dims->channels;
+    const int32_t input_offset = conv_params->in_offset;
+    const int32_t out_offset = conv_params->out_offset;
+    const uint16_t pad_wd = conv_params->padding.width;
+    const uint16_t pad_ht = conv_params->padding.height;
+    const uint16_t stride_wd = conv_params->stride.width;
+    const uint16_t stride_ht = conv_params->stride.height;
+    const uint16_t filter_wd = filter_dims->width;
+    const uint16_t filter_ht = filter_dims->height;
+    const uint16_t out_wd = output_dims->width;
+    const uint16_t out_ht = output_dims->height;
+    const int32_t *out_shift = quant_data->shift;
+    const int32_t *out_mult = quant_data->mult;
+    const int32_t activation_min = conv_params->activation.min;
+    const int32_t activation_max = conv_params->activation.max;
+    const uint16_t ch_mult = conv_params->ch_mult;
+
     int out_idx = 0;
     for (int out_y = 0; out_y < out_ht; out_y++) { //height loop
         const int16_t base_y = (out_y * stride_ht) - pad_ht;

code/components/esp-nn/src/convolution/esp_nn_depthwise_conv_opt.c

@@ -0,0 +1,291 @@
+// Copyright 2020-2021 Espressif Systems (Shanghai) PTE LTD
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include <esp_nn_defs.h>
+#include <common_functions.h>
+
+int esp_nn_get_depthwise_conv_scratch_size_opt(const data_dims_t *input_dims,
+                                               const data_dims_t *filter_dims,
+                                               const data_dims_t *output_dims,
+                                               const dw_conv_params_t *conv_params)
+{
+    return 0;
+}
+
+void esp_nn_set_depthwise_conv_scratch_buf_opt(const void *buf)
+{
+
+}
+
+/* common channel multiplier == 1 case */
+__attribute__ ((noinline))
+static void esp_nn_depthwise_conv_s8_ch_mult_1(const data_dims_t *input_dims,
+                                               const int8_t *input_data,
+                                               const data_dims_t *filter_dims,
+                                               const int8_t *filter_data,
+                                               const int32_t *bias,
+                                               const data_dims_t *output_dims,
+                                               int8_t *out_data,
+                                               const dw_conv_params_t *conv_params,
+                                               const quant_data_t *quant_data)
+{
+    const uint16_t input_wd = input_dims->width;
+    const uint16_t input_ht = input_dims->height;
+    const uint16_t channels = input_dims->channels;
+    const int32_t input_offset = conv_params->in_offset;
+    const int32_t out_offset = conv_params->out_offset;
+    const uint16_t pad_wd = conv_params->padding.width;
+    const uint16_t pad_ht = conv_params->padding.height;
+    const uint16_t stride_wd = conv_params->stride.width;
+    const uint16_t stride_ht = conv_params->stride.height;
+    const uint16_t filter_wd = filter_dims->width;
+    const uint16_t filter_ht = filter_dims->height;
+    const uint16_t out_wd = output_dims->width;
+    const uint16_t out_ht = output_dims->height;
+    const int32_t activation_min = conv_params->activation.min;
+    const int32_t activation_max = conv_params->activation.max;
+
+    int out_idx = 0;
+    for (int out_y = 0; out_y < out_ht; out_y++) { //height loop
+        const int16_t base_y = (out_y * stride_ht) - pad_ht;
+        for (int out_x = 0; out_x < out_wd; out_x++) { //width_loop
+            const int16_t base_x = (out_x * stride_wd) - pad_wd;
+
+            const int32_t *out_shift = quant_data->shift;
+            const int32_t *out_mult = quant_data->mult;
+
+            /* Select filter so as the point doesn't lie outside block */
+            int filter_y_start = max(0, -base_y);
+            int filter_x_start = max(0, -base_x);
+            int filter_y_end = min(filter_ht, input_ht - base_y);
+            int filter_x_end = min(filter_wd, input_wd - base_x);
+
+            int ch_idx = 0;
+            for (; ch_idx < channels - 3; ch_idx += 4) {//channel_loop
+                int32_t result0 = 0;
+                int32_t result1 = 0;
+                int32_t result2 = 0;
+                int32_t result3 = 0;
+
+                for (int filter_y_idx = filter_y_start; filter_y_idx < filter_y_end; filter_y_idx++) {
+                    const int32_t idx_y = base_y + filter_y_idx;
+                    for (int filter_x_idx = filter_x_start; filter_x_idx < filter_x_end; filter_x_idx++) {
+                        const int32_t idx_x = base_x + filter_x_idx;
+                        int32_t input_index = (idx_y * input_wd + idx_x) * channels + ch_idx;
+                        int32_t filter_index = (filter_y_idx * filter_wd + filter_x_idx) * (channels) + ch_idx;
+                        int32_t input_val0 = input_data[input_index + 0] + input_offset;
+                        int32_t input_val1 = input_data[input_index + 1] + input_offset;
+                        int32_t input_val2 = input_data[input_index + 2] + input_offset;
+                        int32_t input_val3 = input_data[input_index + 3] + input_offset;
+                        int32_t filter_val0 = filter_data[filter_index + 0];
+                        int32_t filter_val1 = filter_data[filter_index + 1];
+                        int32_t filter_val2 = filter_data[filter_index + 2];
+                        int32_t filter_val3 = filter_data[filter_index + 3];
+                        result0 += input_val0 * filter_val0;
+                        result1 += input_val1 * filter_val1;
+                        result2 += input_val2 * filter_val2;
+                        result3 += input_val3 * filter_val3;
+                    }
+                }
+                if (bias) {
+                    result0 += bias[ch_idx + 0];
+                    result1 += bias[ch_idx + 1];
+                    result2 += bias[ch_idx + 2];
+                    result3 += bias[ch_idx + 3];
+                }
+                result0 = esp_nn_multiply_by_quantized_mult_fast(result0, *out_mult++, *out_shift++);
+                result1 = esp_nn_multiply_by_quantized_mult_fast(result1, *out_mult++, *out_shift++);
+                result2 = esp_nn_multiply_by_quantized_mult_fast(result2, *out_mult++, *out_shift++);
+                result3 = esp_nn_multiply_by_quantized_mult_fast(result3, *out_mult++, *out_shift++);
+
+                result0 += out_offset;
+                result1 += out_offset;
+                result2 += out_offset;
+                result3 += out_offset;
+
+                result0 = max(result0, activation_min);
+                result1 = max(result1, activation_min);
+                result2 = max(result2, activation_min);
+                result3 = max(result3, activation_min);
+
+                result0 = min(result0, activation_max);
+                result1 = min(result1, activation_max);
+                result2 = min(result2, activation_max);
+                result3 = min(result3, activation_max);
+
+                out_data[out_idx++] = result0;
+                out_data[out_idx++] = result1;
+                out_data[out_idx++] = result2;
+                out_data[out_idx++] = result3;
+            }
+            for (; ch_idx < channels; ch_idx++) {//channel_loop
+                int32_t result = 0;
+
+                for (int filter_y_idx = filter_y_start; filter_y_idx < filter_y_end; filter_y_idx++) {
+                    const int32_t idx_y = base_y + filter_y_idx;
+                    for (int filter_x_idx = filter_x_start; filter_x_idx < filter_x_end; filter_x_idx++) {
+                        const int32_t idx_x = base_x + filter_x_idx;
+                        int32_t input_index = (idx_y * input_wd + idx_x) * channels + ch_idx;
+                        int32_t filter_index = (filter_y_idx * filter_wd + filter_x_idx) * (channels) + ch_idx;
+                        int32_t input_val = input_data[input_index] + input_offset;
+                        int32_t filter_val = filter_data[filter_index];
+                        result += input_val * filter_val;
+                    }
+                }
+                if (bias) {
+                    result += bias[ch_idx];
+                }
+                result = esp_nn_multiply_by_quantized_mult_fast(result, *out_mult++, *out_shift++);
+                result += out_offset;
+                result = max(result, activation_min);
+                result = min(result, activation_max);
+
+                out_data[out_idx++] = result;
+            }
+        }
+    }
+}
+
+void esp_nn_depthwise_conv_s8_opt(const data_dims_t *input_dims,
+                                  const int8_t *input_data,
+                                  const data_dims_t *filter_dims,
+                                  const int8_t *filter_data,
+                                  const int32_t *bias,
+                                  const data_dims_t *output_dims,
+                                  int8_t *out_data,
+                                  const dw_conv_params_t *conv_params,
+                                  const quant_data_t *quant_data)
+{
+    const uint16_t ch_mult = conv_params->ch_mult;
+    if (ch_mult == 1) {
+        esp_nn_depthwise_conv_s8_ch_mult_1(input_dims, input_data, filter_dims, filter_data,
+                                           bias, output_dims, out_data, conv_params, quant_data);
+        return;
+    }
+    const uint16_t input_wd = input_dims->width;
+    const uint16_t input_ht = input_dims->height;
+    const uint16_t channels = input_dims->channels;
+    const int32_t input_offset = conv_params->in_offset;
+    const int32_t out_offset = conv_params->out_offset;
+    const uint16_t pad_wd = conv_params->padding.width;
+    const uint16_t pad_ht = conv_params->padding.height;
+    const uint16_t stride_wd = conv_params->stride.width;
+    const uint16_t stride_ht = conv_params->stride.height;
+    const uint16_t filter_wd = filter_dims->width;
+    const uint16_t filter_ht = filter_dims->height;
+    const uint16_t out_wd = output_dims->width;
+    const uint16_t out_ht = output_dims->height;
+    const int32_t activation_min = conv_params->activation.min;
+    const int32_t activation_max = conv_params->activation.max;
+
+    int out_idx = 0;
+    for (int out_y = 0; out_y < out_ht; out_y++) { //height loop
+        const int16_t base_y = (out_y * stride_ht) - pad_ht;
+        for (int out_x = 0; out_x < out_wd; out_x++) { //width_loop
+            const int16_t base_x = (out_x * stride_wd) - pad_wd;
+
+            const int32_t *out_shift = quant_data->shift;
+            const int32_t *out_mult = quant_data->mult;
+
+            /* Select filter so as the point doesn't lie outside block */
+            int filter_y_start = max(0, -base_y);
+            int filter_x_start = max(0, -base_x);
+            int filter_y_end = min(filter_ht, input_ht - base_y);
+            int filter_x_end = min(filter_wd, input_wd - base_x);
+
+            for (int ch_idx = 0; ch_idx < channels; ch_idx++) {//channel_loop
+                int ch_mult_idx = 0;
+                for (; ch_mult_idx < ch_mult - 3; ch_mult_idx += 4) {
+                    int32_t result0 = 0;
+                    int32_t result1 = 0;
+                    int32_t result2 = 0;
+                    int32_t result3 = 0;
+                    const int out_ch_idx =  ch_idx * ch_mult + ch_mult_idx;
+
+                    for (int filter_y_idx = filter_y_start; filter_y_idx < filter_y_end; filter_y_idx++) {
+                        const int32_t idx_y = base_y + filter_y_idx;
+                        for (int filter_x_idx = filter_x_start; filter_x_idx < filter_x_end; filter_x_idx++) {
+                            const int32_t idx_x = base_x + filter_x_idx;
+                            int32_t input_index = (idx_y * input_wd + idx_x) * channels + ch_idx;
+                            int32_t filter_index = (filter_y_idx * filter_wd + filter_x_idx) * (channels * ch_mult) + out_ch_idx;
+                            int32_t input_val = input_data[input_index] + input_offset;
+                            int32_t filter_val0 = filter_data[filter_index + 0];
+                            int32_t filter_val1 = filter_data[filter_index + 1];
+                            int32_t filter_val2 = filter_data[filter_index + 2];
+                            int32_t filter_val3 = filter_data[filter_index + 3];
+                            result0 += input_val * filter_val0;
+                            result1 += input_val * filter_val1;
+                            result2 += input_val * filter_val2;
+                            result3 += input_val * filter_val3;
+                        }
+                    }
+                    if (bias) {
+                        result0 += bias[out_ch_idx + 0];
+                        result1 += bias[out_ch_idx + 1];
+                        result2 += bias[out_ch_idx + 2];
+                        result3 += bias[out_ch_idx + 3];
+                    }
+                    result0 = esp_nn_multiply_by_quantized_mult_fast(result0, *out_mult++, *out_shift++);
+                    result1 = esp_nn_multiply_by_quantized_mult_fast(result1, *out_mult++, *out_shift++);
+                    result2 = esp_nn_multiply_by_quantized_mult_fast(result2, *out_mult++, *out_shift++);
+                    result3 = esp_nn_multiply_by_quantized_mult_fast(result3, *out_mult++, *out_shift++);
+
+                    result0 += out_offset;
+                    result1 += out_offset;
+                    result2 += out_offset;
+                    result3 += out_offset;
+
+                    result0 = max(result0, activation_min);
+                    result1 = max(result1, activation_min);
+                    result2 = max(result2, activation_min);
+                    result3 = max(result3, activation_min);
+                    result0 = min(result0, activation_max);
+                    result1 = min(result1, activation_max);
+                    result2 = min(result2, activation_max);
+                    result3 = min(result3, activation_max);
+
+                    out_data[out_idx++] = result0;
+                    out_data[out_idx++] = result1;
+                    out_data[out_idx++] = result2;
+                    out_data[out_idx++] = result3;
+                }
+                for (; ch_mult_idx < ch_mult; ch_mult_idx++) {
+                    int32_t result = 0;
+                    const int out_ch_idx =  ch_idx * ch_mult + ch_mult_idx;
+
+                    for (int filter_y_idx = filter_y_start; filter_y_idx < filter_y_end; filter_y_idx++) {
+                        const int32_t idx_y = base_y + filter_y_idx;
+                        for (int filter_x_idx = filter_x_start; filter_x_idx < filter_x_end; filter_x_idx++) {
+                            const int32_t idx_x = base_x + filter_x_idx;
+                            int32_t input_index = (idx_y * input_wd + idx_x) * channels + ch_idx;
+                            int32_t filter_index = (filter_y_idx * filter_wd + filter_x_idx) * (channels * ch_mult) + out_ch_idx;
+                            int32_t input_val = input_data[input_index] + input_offset;
+                            int32_t filter_val = filter_data[filter_index];
+                            result += input_val * filter_val;
+                        }
+                    }
+                    if (bias) {
+                        result += bias[out_ch_idx];
+                    }
+                    result = esp_nn_multiply_by_quantized_mult_fast(result, *out_mult++, *out_shift++);
+                    result += out_offset;
+                    result = max(result, activation_min);
+                    result = min(result, activation_max);
+
+                    out_data[out_idx++] = result;
+                }
+            }
+        }
+    }
+}

code/components/esp-nn/src/convolution/esp_nn_depthwise_conv_s8_esp32s3.c

@@ -12,8 +12,8 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 
-#include <stdint.h>
 #include <stdio.h>
+#include <esp_nn_defs.h>
 
 #include <common_functions.h>
 
@@ -353,17 +353,59 @@ void esp_nn_depthwise_conv_s8_ch_mult1(const int8_t *input_data,
     }
 }
 
-int esp_nn_get_depthwise_conv_scratch_size_esp32s3(const uint16_t input_wd,
-                                                   const uint16_t input_ht,
-                                                   const uint16_t channels,
-                                                   const uint16_t ch_mult,
-                                                   const uint16_t filter_wd,
-                                                   const uint16_t filter_ht)
+int esp_nn_get_depthwise_conv_scratch_size_esp32s3(const data_dims_t *input_dims,
+                                                   const data_dims_t *filter_dims,
+                                                   const data_dims_t *output_dims,
+                                                   const dw_conv_params_t *conv_params)
 {
+    const uint16_t input_wd = input_dims->width;
+    const uint16_t input_ht = input_dims->height;
+    const uint16_t channels = input_dims->channels;
+    const uint16_t filter_wd = filter_dims->width;
+    const uint16_t filter_ht = filter_dims->height;
+    const uint16_t ch_mult = conv_params->ch_mult;
+    const uint16_t out_wd = output_dims->width;
+    const uint16_t out_ht = output_dims->height;
+    const uint16_t pad_wd = conv_params->padding.width;
+    const uint16_t pad_ht = conv_params->padding.height;
+    const uint16_t stride_wd = conv_params->stride.width;
+    const uint16_t stride_ht = conv_params->stride.height;
+
     int filter_size = filter_wd * filter_ht * channels * ch_mult;
-    int padding_used = ((filter_wd == 3) && (filter_ht == 3)) * 2;
-    int input_size = (input_wd + padding_used) * (input_ht + padding_used) * channels;
-    return  2 * (filter_size + input_size) + 16; //16 for alignment
+    int pad_width = 0, pad_height = 0;
+
+    if ((ch_mult == 1) && (channels % 8 == 0) && (filter_wd == 3) && (filter_ht == 3)) {
+        if (channels % 16 == 0) {
+            if (pad_wd || pad_ht) {
+                pad_width = pad_wd * 2;
+                pad_height = pad_ht * 2;
+            } else {
+                // check if we need to pad additionally
+                pad_width = (out_wd * stride_wd + filter_wd - 1) - input_wd;
+                pad_height = (out_ht * stride_ht + filter_ht - 1) - input_ht;
+                // printf("in(%d %d %d), out(%d %d), filter (%d %d) stride (%d %d), pad (%d %d)",
+                //         input_wd, input_ht, channels, out_wd, out_ht, filter_wd, filter_ht,
+                //         stride_wd, stride_ht, pad_wd, pad_ht);
+            }
+            if (pad_width || pad_height) {
+                int input_size = (input_wd + pad_width) * (input_ht + pad_height) * channels;
+                // printf("ask1 %d\n", filter_size + input_size + 16);
+                return filter_size + input_size + 16;  // 16 for alignment
+            } else {
+                // printf("ask2 %d\n", filter_size + 16);
+                return filter_size + 16;  // 16 for alignment
+            }
+        } else {
+            int input_size = input_wd * input_ht * channels;
+            // printf("ask3 %d\n", 2 * (filter_size + input_size) + 16);
+            return  2 * (filter_size + input_size) + 16; // 16 for alignment
+        }
+    } else if (ch_mult % 4 == 0) {
+        int input_size = input_wd * input_ht * channels;
+        // printf("ask4 %d\n", 2 * (filter_size + input_size) + 16);
+        return  2 * (filter_size + input_size) + 16; // 16 for alignment
+    }
+    return 32; // just few bytes
 }
 
 void esp_nn_set_depthwise_conv_scratch_buf_esp32s3(void *buf)
@@ -376,29 +418,38 @@ void esp_nn_set_depthwise_conv_scratch_buf_esp32s3(void *buf)
  * Assumption 2: Pointers are valid
  * Assumption 3: dialation width = 1
  */
-void esp_nn_depthwise_conv_s8_esp32s3(const int8_t *input_data,
-                                      const uint16_t input_wd,
-                                      const uint16_t input_ht,
-                                      const uint16_t channels,
-                                      const int32_t input_offset,
-                                      const uint16_t pad_wd,
-                                      const uint16_t pad_ht,
-                                      const uint16_t stride_wd,
-                                      const uint16_t stride_ht,
-                                      const uint16_t ch_mult,
+
+
+
+void esp_nn_depthwise_conv_s8_esp32s3(const data_dims_t *input_dims,
+                                      const int8_t *input_data,
+                                      const data_dims_t *filter_dims,
                                       const int8_t *filter_data,
-                                      const uint16_t filter_wd,
-                                      const uint16_t filter_ht,
                                       const int32_t *bias,
+                                      const data_dims_t *output_dims,
                                       int8_t *out_data,
-                                      const uint16_t out_wd,
-                                      const uint16_t out_ht,
-                                      const int32_t out_offset,
-                                      const int32_t *out_shift,
-                                      const int32_t *out_mult,
-                                      const int32_t activation_min,
-                                      const int32_t activation_max)
+                                      const dw_conv_params_t *conv_params,
+                                      const quant_data_t *quant_data)
 {
+    const uint16_t input_wd = input_dims->width;
+    const uint16_t input_ht = input_dims->height;
+    const uint16_t channels = input_dims->channels;
+    const int32_t input_offset = conv_params->in_offset;
+    const int32_t out_offset = conv_params->out_offset;
+    const uint16_t pad_wd = conv_params->padding.width;
+    const uint16_t pad_ht = conv_params->padding.height;
+    const uint16_t stride_wd = conv_params->stride.width;
+    const uint16_t stride_ht = conv_params->stride.height;
+    const uint16_t filter_wd = filter_dims->width;
+    const uint16_t filter_ht = filter_dims->height;
+    const uint16_t out_wd = output_dims->width;
+    const uint16_t out_ht = output_dims->height;
+    const int32_t *out_shift = quant_data->shift;
+    const int32_t *out_mult = quant_data->mult;
+    const int32_t activation_min = conv_params->activation.min;
+    const int32_t activation_max = conv_params->activation.max;
+    const uint16_t ch_mult = conv_params->ch_mult;
+
     int filter_size = filter_wd * filter_ht * channels * ch_mult;
     int align_len = 16 - (filter_size & 15);
     int input_size = input_wd * input_ht * channels;
@@ -423,18 +474,27 @@ void esp_nn_depthwise_conv_s8_esp32s3(const int8_t *input_data,
                                                                   stride_wd, stride_ht, filter_aligned, bias,
                                                                   out_data, out_wd, out_ht, out_offset, out_shift,
                                                                   out_mult, activation_min, activation_max);
-            } else if ((pad_wd == 0) && (pad_ht == 0) &&
-                    // because this does not handle padding offset cases yet, run just for stride (1, 1).
-                    // end padding of input with `-input_offset` should solve this
-                    (stride_wd == 1) && (stride_ht == 1)) {
+            } else if ((channels % 16 == 0) && (pad_wd == 0) && (pad_ht == 0)) {
                 /* process in 8 bits */
                 int8_t *filter_aligned = (int8_t *) scratch_buffer;
+                int8_t *input_padded = (int8_t *) scratch_buffer + filter_size + align_len;
+
+                // check if we need to pad additionally
+                int pad_right = (out_wd * stride_wd + filter_wd - 1) - input_wd;
+                int pad_bottom = (out_ht * stride_ht + filter_ht - 1) - input_ht;
+                if (pad_right || pad_bottom) { // pad right and bottom
+                    esp_nn_aligned_s8_pad_end_with_value(input_data, input_padded, input_wd, input_ht,
+                                                         channels, -input_offset, pad_right, pad_bottom);
+                } else {
+                    input_padded = (int8_t *) input_data;
+                }
                 memcpy(filter_aligned, filter_data, filter_size);
-                esp_nn_depthwise_conv_s8_mult1_3x3_padded_esp32s3(input_data, input_wd, input_ht, channels, input_offset,
-                                                                  stride_wd, stride_ht, filter_aligned,
-                                                                  bias, out_data, out_wd, out_ht, out_offset, out_shift,
+                esp_nn_depthwise_conv_s8_mult1_3x3_padded_esp32s3(input_padded, input_wd + pad_right,
+                                                                  input_ht + pad_bottom, channels, input_offset,
+                                                                  stride_wd, stride_ht, filter_aligned, bias,
+                                                                  out_data, out_wd, out_ht, out_offset, out_shift,
                                                                   out_mult, activation_min, activation_max);
-            } else { /* (channels % 8) == 0 && pad_wd == 1 && pad_ht == 1 */
+            } else { /* (channels % 8) == 0 */
                 esp_nn_s8_to_s16_esp32s3(filter_data, filter_data16, filter_size);
                 esp_nn_aligned_s8_to_s16_with_offset_esp32s3(input_data, input_data16, input_size, input_offset);
                 esp_nn_depthwise_conv_s16_mult1_3x3_esp32s3(input_data16, input_wd, input_ht, channels,

code/components/esp-nn/test_app/sdkconfig.defaults.esp32s3

@@ -0,0 +1,8 @@
+# Default configurations for ESP32-S3
+
+CONFIG_ESP32S3_DEFAULT_CPU_FREQ_240=y
+CONFIG_ESP32S3_SPIRAM_SUPPORT=y
+
+CONFIG_ESP32S3_DATA_CACHE_64KB=y
+CONFIG_ESP32S3_DATA_CACHE_8WAYS=y
+CONFIG_ESP32S3_DATA_CACHE_LINE_64B=y

code/components/esp-nn/tests/src/basic_math_test.c

@@ -23,7 +23,9 @@
 #include "test_utils.h"
 
 #if CONFIG_IDF_CMAKE
+#if (CONFIG_SPIRAM_SUPPORT && (CONFIG_SPIRAM_USE_CAPS_ALLOC || CONFIG_SPIRAM_USE_MALLOC))
 #define IDF_HEAP_CAPS 1
+#endif
 
 #if IDF_HEAP_CAPS
 #include "esp_heap_caps.h"
@@ -138,6 +140,11 @@ void esp_nn_add_elementwise_s8_test()
         out_c_orig = out_data_c;
         out_opt_orig = out_data_opt;
 #endif
+        if (input1_orig == NULL || input2_orig == NULL || out_c_orig == NULL ||
+                out_opt_orig == NULL) {
+            printf(ANSI_COLOR_RED"%s error allocating buffers\n"ANSI_COLOR_RESET, __FUNCTION__);
+            goto elementwise_add_test_cleanup;
+        }
 
         for (int i = 0; i < size; ++i) {
             input1[i] = rand() % 256 - 128;
@@ -194,10 +201,10 @@ elementwise_add_test_cleanup:
         if (input2_orig) {
             free(input2_orig);
         }
-        if (out_data_c) {
+        if (out_c_orig) {
             free(out_c_orig);
         }
-        if (out_data_opt) {
+        if (out_opt_orig) {
             free(out_opt_orig);
         }
     }
@@ -282,6 +289,11 @@ void esp_nn_mul_elementwise_s8_test()
         out_c_orig = out_data_c;
         out_opt_orig = out_data_opt;
 #endif
+        if (input1_orig == NULL || input2_orig == NULL || out_c_orig == NULL ||
+                out_opt_orig == NULL) {
+            printf(ANSI_COLOR_RED"%s error allocating buffers\n"ANSI_COLOR_RESET, __FUNCTION__);
+            goto elementwise_mult_test_cleanup;
+        }
 
         for (int i = 0; i < size; ++i) {
             input1[i] = rand() % 256 - 128;
@@ -333,10 +345,10 @@ elementwise_mult_test_cleanup:
         if (input2_orig) {
             free(input2_orig);
         }
-        if (out_data_c) {
+        if (out_c_orig) {
             free(out_c_orig);
         }
-        if (out_data_opt) {
+        if (out_opt_orig) {
             free(out_opt_orig);
         }
     }

code/components/esp-nn/tests/src/convolution_test.c

@@ -22,8 +22,9 @@
 #include "test_utils.h"
 
 #if CONFIG_IDF_CMAKE
+#if (CONFIG_SPIRAM_SUPPORT && (CONFIG_SPIRAM_USE_CAPS_ALLOC || CONFIG_SPIRAM_USE_MALLOC))
 #define IDF_HEAP_CAPS 1
-
+#endif
 #if IDF_HEAP_CAPS
 #include "esp_heap_caps.h"
 #endif
@@ -44,8 +45,8 @@ void esp_nn_depthwise_conv_s8_test()
     uint16_t filter_ht, filter_wd, ch_mult;
     uint16_t pad_wd, pad_ht, stride_wd, stride_ht;
 
-    // run for 10 iterations
-    for (int itr = 0; itr < 10; itr++) {
+    // run for 15 iterations
+    for (int itr = 0; itr < 15; itr++) {
         /* prepare data */
         switch (itr) {
         case 0: // (ch_mult 1, (channels % 16) = 0), filter (3,3), pad (0,0)
@@ -144,22 +145,52 @@ void esp_nn_depthwise_conv_s8_test()
             stride_wd = 2;
             stride_ht = 2;
             break;
-        default:
-            input_wd = 4;
-            input_ht = 4;
+        case 8: // same as case 7, with large parameters
+            input_wd = 58;
+            input_ht = 58;
             filter_ht = 3;
             filter_wd = 3;
-            ch_mult = 4;
-            channels = 4;
-            pad_wd = 1;
-            pad_ht = 1;
-            stride_wd = 1;
-            stride_ht = 1;
+            ch_mult = 1;
+            channels = 128;
+            pad_wd = 0;
+            pad_ht = 0;
+            stride_wd = 2;
+            stride_ht = 2;
+            break;
+        case 9: // (ch_mult 1, (channels % 16) = 0), filter (3,3), pad (0,0)  stride (2,2)
+            input_wd = 6;
+            input_ht = 6;
+            filter_ht = 3;
+            filter_wd = 3;
+            ch_mult = 1;
+            channels = 16;
+            pad_wd = 0;
+            pad_ht = 0;
+            stride_wd = 2;
+            stride_ht = 2;
+            break;
+        default:
+            input_wd = 6;
+            input_ht = 6;
+            filter_ht = 3;
+            filter_wd = 3;
+            ch_mult = 1;
+            channels = 16;
+            stride_wd = rand() % 2 + 1;
+            stride_ht = stride_wd;
+            pad_wd = stride_wd == 1 ? 0 : rand() % 2;
+            pad_ht = pad_wd;
+            printf("stride(%d), pad (%d)\t", stride_wd, pad_wd);
             break;
         }
 
         uint16_t out_wd = (input_wd - filter_wd + 1) / stride_wd;
         uint16_t out_ht = (input_ht - filter_ht + 1) / stride_ht;
+        if (itr == 9) {
+            // expect the function to handle this gracefully
+            out_wd += 1;
+            out_ht += 1;
+        }
         int in_size = input_wd * input_ht * channels;
         int out_size = out_wd * out_ht * channels * ch_mult;
         int filter_size = filter_wd * filter_ht * channels * ch_mult + 4;
@@ -210,9 +241,16 @@ void esp_nn_depthwise_conv_s8_test()
             out_mult[i] = 0x7eb0e200 + rand() % 50;
         }
 
-        int scratch_buf_size = esp_nn_get_depthwise_conv_scratch_size(input_wd, input_ht,
-                                                                    channels, ch_mult,
-                                                                    filter_wd, filter_ht);
+        data_dims_t input_dims = {.width = input_wd, .height = input_ht, .channels = channels, 1};
+        data_dims_t output_dims = {.width = out_wd, .height = out_ht, .channels = channels * ch_mult, 1};
+        data_dims_t filter_dims = {.width = filter_wd, .height = filter_ht, 0, 0};
+        dw_conv_params_t conv_params = {.in_offset = input_offset, .out_offset = out_offset, .ch_mult = ch_mult,
+                                        .stride = {stride_wd, stride_ht}, .padding = {pad_wd, pad_ht},
+                                        .dilation = {0, 0}, .activation = {activation_min, activation_max}};
+        quant_data_t quant_data = {.shift = out_shift, .mult = out_mult};
+
+        int scratch_buf_size = esp_nn_get_depthwise_conv_scratch_size(&input_dims, &filter_dims,
+                                                                      &output_dims, &conv_params);
         if (scratch_buf_size > 0) {
 #if IDF_HEAP_CAPS
             scratch_buf = heap_caps_malloc(scratch_buf_size + 32, MALLOC_CAP_SPIRAM | MALLOC_CAP_8BIT);
@@ -234,11 +272,8 @@ void esp_nn_depthwise_conv_s8_test()
         }
 
         /* C function */
-        esp_nn_depthwise_conv_s8_ansi(input, input_wd, input_ht, channels, input_offset,
-                                    pad_wd, pad_ht, stride_wd, stride_ht, ch_mult,
-                                    filter_data + 4, filter_wd, filter_ht,
-                                    bias + 1, out_data_c, out_wd, out_ht, out_offset, out_shift,
-                                    out_mult, activation_min, activation_max);
+        esp_nn_depthwise_conv_s8_ansi(&input_dims, input, &filter_dims, filter_data + 4,
+                                      bias + 1, &output_dims, out_data_c, &conv_params, &quant_data);
 
         if (itr == 0) {
             profile_c_end();
@@ -246,11 +281,8 @@ void esp_nn_depthwise_conv_s8_test()
         }
 
         /* Optimized function */
-        esp_nn_depthwise_conv_s8(input, input_wd, input_ht, channels, input_offset,
-                                pad_wd, pad_ht, stride_wd, stride_ht, ch_mult,
-                                filter_data + 4, filter_wd, filter_ht,
-                                bias + 1, out_data_opt, out_wd, out_ht, out_offset, out_shift,
-                                out_mult, activation_min, activation_max);
+        esp_nn_depthwise_conv_s8(&input_dims, input, &filter_dims, filter_data + 4,
+                                 bias + 1, &output_dims, out_data_opt, &conv_params, &quant_data);
 
         if (itr == 0) {
             /* disable profiler */
@@ -479,8 +511,16 @@ void esp_nn_conv_s8_test()
             out_mult[i] = 0x7f67f4f8 + rand() % 50;
         }
 
-        int scratch_buf_size = esp_nn_get_conv_scratch_size(in_wd, in_ht, in_channels,
-                                                            out_channels, filter_wd, filter_ht);
+        data_dims_t input_dims = {.width = in_wd, .height = in_ht, .channels = in_channels, 1};
+        data_dims_t output_dims = {.width = out_wd, .height = out_ht, .channels = out_channels, 1};
+        data_dims_t filter_dims = {.width = filter_wd, .height = filter_ht, 0, 0};
+        conv_params_t conv_params = {.in_offset = input_offset, .out_offset = out_offset,
+                                    .stride = {stride_wd, stride_ht}, .padding = {pad_wd, pad_ht},
+                                    .dilation = {0, 0}, .activation = {activation_min, activation_max}};
+        quant_data_t quant_data = {.shift = out_shift, .mult = out_mult};
+
+        int scratch_buf_size = esp_nn_get_conv_scratch_size(&input_dims, &filter_dims,
+                                                            &output_dims, &conv_params);
         if (scratch_buf_size > 0) {
 #if IDF_HEAP_CAPS
             void *scratch_buf = heap_caps_malloc(scratch_buf_size + 32, MALLOC_CAP_SPIRAM | MALLOC_CAP_8BIT);
@@ -502,11 +542,8 @@ void esp_nn_conv_s8_test()
         }
 
         /* C function */
-        esp_nn_conv_s8_ansi(input, in_wd, in_ht, in_channels, input_offset,
-                            pad_wd, pad_ht, stride_wd, stride_ht,
-                            filter_data + 2, filter_wd, filter_ht, bias,
-                            out_data_c, out_wd, out_ht, out_channels, out_offset, out_shift,
-                            out_mult, activation_min, activation_max);
+        esp_nn_conv_s8_ansi(&input_dims, input, &filter_dims, filter_data + 2,
+                            bias, &output_dims, out_data_c, &conv_params, &quant_data);
 
         if (itr == 0) {
             profile_c_end();
@@ -514,11 +551,8 @@ void esp_nn_conv_s8_test()
         }
 
         /* Optimized function */
-        esp_nn_conv_s8(input, in_wd, in_ht, in_channels, input_offset,
-                    pad_wd, pad_ht, stride_wd, stride_ht,
-                    filter_data + 2, filter_wd, filter_ht, bias,
-                    out_data_opt, out_wd, out_ht, out_channels, out_offset, out_shift,
-                    out_mult, activation_min, activation_max);
+        esp_nn_conv_s8(&input_dims, input, &filter_dims, filter_data + 2,
+                       bias, &output_dims, out_data_opt, &conv_params, &quant_data);
 
         if (itr == 0) {
             /* disable profiler */

code/components/jomjol_controlcamera/ClassControllCamera.cpp

@@ -263,6 +263,9 @@ void CCamera::EnableAutoExposure(int flashdauer)
         ESP_LOGE(TAGCAMERACLASS, "Camera Capture Failed");
         LEDOnOff(false);
         LightOnOff(false);
+        LogFile.SwitchOnOff(true);
+        LogFile.WriteToFile("Camera Capture Failed (Procedure 'EnableAutoExposure') --> Reboot"
+                "Check that your camera module is working and connected properly.");
         doReboot();
     }
     esp_camera_fb_return(fb);        
@@ -313,7 +316,7 @@ esp_err_t CCamera::CaptureToBasisImage(CImageBasis *_Image, int delay)
         LightOnOff(false);
 
         LogFile.SwitchOnOff(true);
-        LogFile.WriteToFile("Camera is not working anymore - most propably hardware problem (instablility, ...). "
+        LogFile.WriteToFile("Camera is not working anymore (CCamera::CaptureToBasisImage) - most propably hardware problem (instablility, ...). "
                 "System will reboot.");
         doReboot();
 
@@ -410,6 +413,9 @@ esp_err_t CCamera::CaptureToFile(std::string nm, int delay)
         ESP_LOGE(TAGCAMERACLASS, "CaptureToFile: Camera Capture Failed");
         LEDOnOff(false);
         LightOnOff(false);
+        LogFile.SwitchOnOff(true);
+        LogFile.WriteToFile("Camera Capture Failed (CCamera::CaptureToFile) --> Reboot"
+                "Check that your camera module is working and connected properly.");
         doReboot();
 
         return ESP_FAIL;

code/components/jomjol_fileserver_ota/server_ota.cpp

@@ -416,6 +416,8 @@ void task_reboot(void *pvParameter)
 }
 
 void doReboot(){
+    LogFile.SwitchOnOff(true);
+    LogFile.WriteToFile("Reboot triggert by Software (5s).");
     ESP_LOGI(TAGPARTOTA, "Reboot in 5sec");
     LogFile.WriteToFile("Reboot in 5sec");
     xTaskCreate(&task_reboot, "reboot", configMINIMAL_STACK_SIZE * 64, NULL, 10, NULL);
@@ -435,7 +437,7 @@ esp_err_t handler_reboot(httpd_req_t *req)
 
     LogFile.WriteToFile("handler_reboot");
     ESP_LOGI(TAGPARTOTA, "!!! System will restart within 5 sec!!!");
-    const char* resp_str = "!!! System will restart within 5 sec!!!";
+    const char* resp_str = "<body style='font-family: arial'> <h3 id=t></h3></body><script>var h='Rebooting!<br>The page will automatically reload.<br>'; document.getElementById('t').innerHTML=h; setInterval(function (){h +='.'; document.getElementById('t').innerHTML=h; fetch(window.location.hostname,{mode: 'no-cors'}).then(r=>{window.location.replace('/wasserzaehler_roi.html');})}, 1000);</script>";
     httpd_resp_send(req, resp_str, strlen(resp_str)); 
     
     doReboot();

code/components/jomjol_flowcontroll/ClassFlowCNNGeneral.cpp

@@ -28,7 +28,7 @@ ClassFlowCNNGeneral::ClassFlowCNNGeneral(ClassFlowAlignment *_flowalign, t_CNNTy
     flowpostalignment = _flowalign;
 }
 
-string ClassFlowCNNGeneral::getReadout(int _analog = 0, bool _extendedResolution, int prev)
+string ClassFlowCNNGeneral::getReadout(int _analog = 0, bool _extendedResolution, int prev, float _vorgaengerAnalog)
 {
     string result = "";    
 
@@ -41,8 +41,8 @@ string ClassFlowCNNGeneral::getReadout(int _analog = 0, bool _extendedResolution
         float zahl = GENERAL[_analog]->ROI[GENERAL[_analog]->ROI.size() - 1]->result_float;
         int ergebnis_nachkomma = ((int) floor(zahl * 10) + 10) % 10;
         
-        prev = ZeigerEval(GENERAL[_analog]->ROI[GENERAL[_analog]->ROI.size() - 1]->result_float, prev);
-        if (debugdetailgeneral) LogFile.WriteToFile("ClassFlowCNNGeneral::getReadout(analog) zahl=" + std::to_string(zahl) + ", ergebnis_nachkomma=" + std::to_string(ergebnis_nachkomma) + ", prev=" + std::to_string(prev));
+        prev = ZeigerEvalAnalogNeu(GENERAL[_analog]->ROI[GENERAL[_analog]->ROI.size() - 1]->result_float, prev);
+//        if (debugdetailgeneral) LogFile.WriteToFile("ClassFlowCNNGeneral::getReadout(analog) zahl=" + std::to_string(zahl) + ", ergebnis_nachkomma=" + std::to_string(ergebnis_nachkomma) + ", prev=" + std::to_string(prev));
         result = std::to_string(prev);
 
         if (_extendedResolution && (CNNType != Digital))
@@ -50,7 +50,7 @@ string ClassFlowCNNGeneral::getReadout(int _analog = 0, bool _extendedResolution
 
         for (int i = GENERAL[_analog]->ROI.size() - 2; i >= 0; --i)
         {
-            prev = ZeigerEval(GENERAL[_analog]->ROI[i]->result_float, prev);
+            prev = ZeigerEvalAnalogNeu(GENERAL[_analog]->ROI[i]->result_float, prev);
             result = std::to_string(prev) + result;
         }
         return result;
@@ -82,13 +82,14 @@ string ClassFlowCNNGeneral::getReadout(int _analog = 0, bool _extendedResolution
                 result = std::to_string(ergebnis_vorkomma) + std::to_string(ergebnis_nachkomma);
                 prev = ergebnis_vorkomma;
                 if (debugdetailgeneral) LogFile.WriteToFile("ClassFlowCNNGeneral::getReadout(dig100-ext) ergebnis_vorkomma=" + std::to_string(ergebnis_vorkomma) + ", ergebnis_nachkomma=" + std::to_string(ergebnis_nachkomma) + ", prev=" + std::to_string(prev));
-        
-
             }
             else
             {
 //                prev = ZeigerEval(GENERAL[_analog]->ROI[GENERAL[_analog]->ROI.size() - 1]->result_float, prev);
-                prev = ZeigerEvalHybrid(GENERAL[_analog]->ROI[GENERAL[_analog]->ROI.size() - 1]->result_float, prev, prev);
+                if (_vorgaengerAnalog >= 0)
+                    prev = ZeigerEvalHybridNeu(GENERAL[_analog]->ROI[GENERAL[_analog]->ROI.size() - 1]->result_float, _vorgaengerAnalog, prev, true);
+                else
+                    prev = ZeigerEvalHybridNeu(GENERAL[_analog]->ROI[GENERAL[_analog]->ROI.size() - 1]->result_float, prev, prev);
                 result = std::to_string(prev);
                 if (debugdetailgeneral) LogFile.WriteToFile("ClassFlowCNNGeneral::getReadout(dig100)  prev=" + std::to_string(prev));
         
@@ -105,9 +106,11 @@ string ClassFlowCNNGeneral::getReadout(int _analog = 0, bool _extendedResolution
         {
             if (GENERAL[_analog]->ROI[i]->result_float >= 0)
             {
-                prev = ZeigerEvalHybrid(GENERAL[_analog]->ROI[i]->result_float, GENERAL[_analog]->ROI[i+1]->result_float, prev);
+                prev = ZeigerEvalHybridNeu(GENERAL[_analog]->ROI[i]->result_float, GENERAL[_analog]->ROI[i+1]->result_float, prev);
+                if (debugdetailgeneral) LogFile.WriteToFile("ClassFlowCNNGeneral::getReadout#ZeigerEvalHybridNeu()= " + std::to_string(prev));
                 result = std::to_string(prev) + result;
-
+                if (debugdetailgeneral) LogFile.WriteToFile("ClassFlowCNNGeneral::getReadout#result= " + result);
+                
             }
             else
             {
@@ -120,57 +123,15 @@ string ClassFlowCNNGeneral::getReadout(int _analog = 0, bool _extendedResolution
         return result;
     }
 
-/*
-    if (CNNType == Digital100)
-    {
-        int zif_akt = -1;
-
-        float zahl = GENERAL[_analog]->ROI[GENERAL[_analog]->ROI.size() - 1]->result_float;
-        if (zahl >= 0)       // NaN?
-        {
-            if (_extendedResolution)
-            {
-                int ergebnis_nachkomma = ((int) floor(zahl * 10)) % 10;
-                int ergebnis_vorkomma = ((int) floor(zahl)) % 10;
-
-                result = std::to_string(ergebnis_vorkomma) + std::to_string(ergebnis_nachkomma);
-                zif_akt = ergebnis_vorkomma;
-            }
-            else
-            {
-                zif_akt = ZeigerEvalHybrid(GENERAL[_analog]->ROI[GENERAL[_analog]->ROI.size() - 1]->result_float, -1, -1);
-                result = std::to_string(zif_akt);
-            }
-        }
-        else
-        {
-            result = "N";
-            if (_extendedResolution && (CNNType != Digital))
-                result = "NN";
-        }
-
-        for (int i = GENERAL[_analog]->ROI.size() - 2; i >= 0; --i)
-        {
-            if (GENERAL[_analog]->ROI[i]->result_float >= 0)
-            {
-                zif_akt = ZeigerEvalHybrid(GENERAL[_analog]->ROI[i]->result_float, GENERAL[_analog]->ROI[i+1]->result_float, zif_akt);
-                result = std::to_string(zif_akt) + result;
-            }
-            else
-            {
-                zif_akt = -1;
-                result = "N" + result;
-            }
-        }
-        return result;
-    }
-*/
 
     return result;
 }
 
+/*
 int ClassFlowCNNGeneral::ZeigerEvalHybrid(float zahl, float zahl_vorgaenger, int eval_vorgaenger)
 {
+    if (debugdetailgeneral) LogFile.WriteToFile("ClassFlowCNNGeneral::ZeigerEvalHybrid( " + std::to_string(zahl) + ", " + std::to_string(zahl_vorgaenger) + ", " + std::to_string(eval_vorgaenger) + ")");
+                
     int ergebnis_nachkomma = ((int) floor(zahl * 10)) % 10;
     int ergebnis_vorkomma = ((int) floor(zahl) + 10) % 10;
 
@@ -183,10 +144,15 @@ int ClassFlowCNNGeneral::ZeigerEvalHybrid(float zahl, float zahl_vorgaenger, int
             return ((int) trunc(zahl) + 10) % 10;
     }
 
+    // 9.0, da bei getReadout() prev als int übergeben wird (9 statt 9.5)
+    // tritt bei der ersten ziffer von digit auf, wenn analog davor (2. Aufruf von getReadout)
     if ((zahl_vorgaenger >= 0.5 ) && (zahl_vorgaenger < 9.5))
     {
         // kein Ziffernwechsel, da Vorkomma weit genug weg ist (0+/-0.5) --> zahl wird gerundet
-        return ((int) round(zahl) + 10) % 10;
+        if ((ergebnis_nachkomma <= 2) || (ergebnis_nachkomma >= 8))     // Band um die Ziffer --> Runden, da Ziffer im Rahmen Ungenauigkeit erreicht
+            return ((int) round(zahl) + 10) % 10;
+        else
+            return ((int) trunc(zahl) + 10) % 10;
     }  
     else
     {
@@ -211,38 +177,169 @@ int ClassFlowCNNGeneral::ZeigerEvalHybrid(float zahl, float zahl_vorgaenger, int
                         + ", zahl_vorgaenger=" + std::to_string(zahl_vorgaenger) + ", eval_vorgaenger=" + std::to_string(eval_vorgaenger));
     return -1;
 
-/*
-    if (zahl_vorgaenger > 9.2)              // Ziffernwechsel beginnt
+}
+*/
+
+int ClassFlowCNNGeneral::ZeigerEvalHybridNeu(float zahl, float zahl_vorgaenger, int eval_vorgaenger, bool AnalogerVorgaenger)
+{
+    int result;
+    int ergebnis_nachkomma = ((int) floor(zahl * 10)) % 10;
+    int ergebnis_vorkomma = ((int) floor(zahl) + 10) % 10;
+
+    if (eval_vorgaenger < 0)
     {
-        if (eval_vorgaenger == 0)           // Wechsel hat schon stattgefunden
-        {
-            return ((int) round(zahl) + 10) % 10;      // Annahme, dass die neue Zahl schon in der Nähe des Ziels ist
-        }
+        if ((ergebnis_nachkomma <= DigitalUnschaerfe * 10) || (ergebnis_nachkomma >= DigitalUnschaerfe * 10))     // Band um die Ziffer --> Runden, da Ziffer im Rahmen Ungenauigkeit erreicht
+            result = (int) (round(zahl) + 10) % 10;
         else
-        {
-            if (zahl_vorgaenger <= 9.5)     // Wechsel startet gerade, aber beginnt erst
-            {
-                if ((ergebnis_nachkomma <= 2) || (ergebnis_nachkomma >= 8))     // Band um die Ziffer --> Runden, da Ziffer im Rahmen Ungenauigkeit erreicht
-                    return ((int) round(zahl) + 10) % 10;
-                else
-                    return ((int) trunc(zahl) + 10) % 10;
-            }
-            else
-            {
-                return ((int) trunc(zahl) + 10) % 10;   // Wechsel schon weiter fortgeschritten, d.h. über 2 als Nachkomma
-            }
-        }
+            result = (int) ((int) trunc(zahl) + 10) % 10;
+
+        if (debugdetailgeneral) LogFile.WriteToFile("ClassFlowCNNGeneral::ZeigerEvalHybridNeu - kein Vorgänger - Ergebnis = " + std::to_string(result) +
+                                                    " zahl: " + std::to_string(zahl) + " zahl_vorgaenger = " + std::to_string(zahl_vorgaenger)+ " eval_vorgaenger = " + std::to_string(eval_vorgaenger) + " DigitalUnschaerfe = " +  std::to_string(DigitalUnschaerfe));
+        return result;
     }
 
-    if ((ergebnis_nachkomma <= 2) || (ergebnis_nachkomma >= 8))     // Band um die Ziffer --> Runden, da Ziffer im Rahmen Ungenauigkeit erreicht
-        return ((int) round(zahl) + 10) % 10;
+    if (AnalogerVorgaenger)
+    {
+//        result = ZeigerEvalAnalogToDigitNeu(zahl, eval_vorgaenger);
+        result = ZeigerEvalAnalogToDigitNeu(zahl, zahl_vorgaenger);
+        if (debugdetailgeneral) LogFile.WriteToFile("ClassFlowCNNGeneral::ZeigerEvalHybridNeu - Analoger Vorgänger, Bewertung über ZeigerEvalAnalogNeu = " + std::to_string(result) +
+                                                    " zahl: " + std::to_string(zahl) + " zahl_vorgaenger = " + std::to_string(zahl_vorgaenger)+ " eval_vorgaenger = " + std::to_string(eval_vorgaenger) + " DigitalUnschaerfe = " +  std::to_string(DigitalUnschaerfe));
+        return result;
+    }
 
-    return ((int) trunc(zahl) + 10) % 10;
-*/
+    if ((zahl_vorgaenger >= DigitalUebergangsbereichVorgaenger ) && (zahl_vorgaenger <= (10.0 - DigitalUebergangsbereichVorgaenger)))
+    {
+        // kein Ziffernwechsel, da Vorgänger weit genug weg ist (0+/-DigitalUebergangsbereichVorgaenger) --> zahl wird gerundet
+        if ((ergebnis_nachkomma <= DigitalBand) || (ergebnis_nachkomma >= (10-DigitalBand)))     // Band um die Ziffer --> Runden, da Ziffer im Rahmen Ungenauigkeit erreicht
+            result = ((int) round(zahl) + 10) % 10;
+        else
+            result = ((int) trunc(zahl) + 10) % 10;
+
+        if (debugdetailgeneral) LogFile.WriteToFile("ClassFlowCNNGeneral::ZeigerEvalHybridNeu - KEIN Analoger Vorgänger, kein Ziffernwechsel, da Vorkomma weit genug weg = " + std::to_string(result) +
+                                                    " zahl: " + std::to_string(zahl) + " zahl_vorgaenger = " + std::to_string(zahl_vorgaenger)+ " eval_vorgaenger = " + std::to_string(eval_vorgaenger) + " DigitalUnschaerfe = " +  std::to_string(DigitalUnschaerfe));
+        return result;
+    }  
+
+    if (eval_vorgaenger <= 1)  // Nulldurchgang hat stattgefunden (!Bewertung über Prev_value und nicht Zahl!) --> hier aufrunden (2.8 --> 3, aber auch 3.1 --> 3)
+    {
+        if (ergebnis_nachkomma > 5)
+            result =  (ergebnis_vorkomma + 1) % 10;
+        else
+            result =  ergebnis_vorkomma;
+        if (debugdetailgeneral) LogFile.WriteToFile("ClassFlowCNNGeneral::ZeigerEvalHybridNeu - KEIN Analoger Vorgänger, Nulldurchgang hat stattgefunden = " + std::to_string(result) +
+                                                    " zahl: " + std::to_string(zahl) + " zahl_vorgaenger = " + std::to_string(zahl_vorgaenger)+ " eval_vorgaenger = " + std::to_string(eval_vorgaenger) + " DigitalUnschaerfe = " +  std::to_string(DigitalUnschaerfe));
+        return result;
+    }
+
+    // bleibt nur >= 9.5 --> noch kein Nulldurchgang --> 2.8 --> 2, und 3.1 --> 2
+    // hier auf 4 reduziert, da erst ab Vorgänder 9 anfängt umzustellen. Bei 9.5 Vorgänger kann die aktuelle
+    // Zahl noch x.4 - x.5 sein.
+    if (ergebnis_nachkomma >= 4)
+        result =  ergebnis_vorkomma;
+    else
+        result =  (ergebnis_vorkomma - 1 + 10) % 10;
+
+    if (debugdetailgeneral) LogFile.WriteToFile("ClassFlowCNNGeneral::ZeigerEvalHybridNeu - KEIN Analoger Vorgänger, >= 9.5 --> noch kein Nulldurchgang = " + std::to_string(result) +
+                                                " zahl: " + std::to_string(zahl) + " zahl_vorgaenger = " + std::to_string(zahl_vorgaenger)+ " eval_vorgaenger = " + std::to_string(eval_vorgaenger) + " DigitalUnschaerfe = " +  std::to_string(DigitalUnschaerfe));
+    return result;
 }
 
 
+int ClassFlowCNNGeneral::ZeigerEvalAnalogToDigitNeu(float zahl, float ziffer_vorgaenger)
+{
+    int result;
+    int ergebnis_nachkomma = ((int) floor(zahl * 10)) % 10;
+    int ergebnis_vorkomma = ((int) floor(zahl) + 10) % 10;
 
+    if (ziffer_vorgaenger < 0)
+    {
+        result = (int) floor(zahl);
+        if (debugdetailgeneral) LogFile.WriteToFile("ClassFlowCNNGeneral::ZeigerEvalAnalogToDigitNeu - kein Vorgänger - Ergebnis = " + std::to_string(result) +
+                                                    " zahl: " + std::to_string(zahl) + " ziffer_vorgaenger = " + std::to_string(ziffer_vorgaenger) + " AnalogFehler = " +  std::to_string(AnalogFehler));
+        return result;
+    }
+
+    if ((ziffer_vorgaenger >= DigitalUebergangsbereichVorgaengerAnalogToDigit ) && (ziffer_vorgaenger <= (10.0 - DigitalUebergangsbereichVorgaengerAnalogToDigit)))
+    {
+        // kein Ziffernwechsel, da Vorgänger weit genug weg ist (0+/-DigitalUebergangsbereichVorgaenger) --> zahl wird gerundet
+        if ((ergebnis_nachkomma <= 2) || (ergebnis_nachkomma >= 8))     // Band um die Ziffer --> Runden, da Ziffer im Rahmen Ungenauigkeit erreicht
+            result = ((int) round(zahl) + 10) % 10;
+        else
+            result = ((int) trunc(zahl) + 10) % 10;
+
+        if (debugdetailgeneral) LogFile.WriteToFile("ClassFlowCNNGeneral::ZeigerEvalAnalogToDigitNeu - kein Ziffernwechsel, da Vorkomma weit genug weg = " + std::to_string(result) +
+                                                    " zahl: " + std::to_string(zahl) + " ziffer_vorgaenger = " + std::to_string(ziffer_vorgaenger) + " DigitalUnschaerfe = " +  std::to_string(DigitalUnschaerfe));
+        return result;
+    }  
+
+    if (ziffer_vorgaenger <= 1)  // Nulldurchgang hat stattgefunden (!Bewertung über Prev_value und nicht Zahl!) --> hier aufrunden (2.8 --> 3, aber auch 3.1 --> 3)
+    {
+        if (ergebnis_nachkomma > 5)
+            result =  (ergebnis_vorkomma + 1) % 10;
+        else
+            result =  ergebnis_vorkomma;
+        if (debugdetailgeneral) LogFile.WriteToFile("ClassFlowCNNGeneral::ZeigerEvalAnalogToDigitNeu - Nulldurchgang hat stattgefunden = " + std::to_string(result) +
+                                                    " zahl: " + std::to_string(zahl) + " ziffer_vorgaenger = " + std::to_string(ziffer_vorgaenger) + " DigitalUnschaerfe = " +  std::to_string(DigitalUnschaerfe));
+        return result;
+    }
+
+    // bleibt nur >= 9.5 --> noch kein Nulldurchgang --> 2.8 --> 2, und 3.1 --> 2
+    // hier auf 4 reduziert, da erst ab Vorgänder 9 anfängt umzustellen. Bei 9.5 Vorgänger kann die aktuelle
+    // Zahl noch x.4 - x.5 sein.
+    if (ergebnis_nachkomma >= 4)
+        result =  ergebnis_vorkomma;
+    else
+        result =  (ergebnis_vorkomma - 1 + 10) % 10;
+
+    if (debugdetailgeneral) LogFile.WriteToFile("ClassFlowCNNGeneral::ZeigerEvalAnalogToDigitNeu - 9.0 --> noch kein Nulldurchgang = " + std::to_string(result) +
+                                                    " zahl: " + std::to_string(zahl) + " ziffer_vorgaenger = " + std::to_string(ziffer_vorgaenger) + " DigitalUnschaerfe = " +  std::to_string(DigitalUnschaerfe));
+    return result;
+}
+
+int ClassFlowCNNGeneral::ZeigerEvalAnalogNeu(float zahl, int ziffer_vorgaenger)
+{
+    float zahl_min, zahl_max;
+    int result;
+
+    if (ziffer_vorgaenger == -1)
+    {
+        result = (int) floor(zahl);
+        if (debugdetailgeneral) LogFile.WriteToFile("ClassFlowCNNGeneral::ZeigerEvalAnalogNeu - kein Vorgänger - Ergebnis = " + std::to_string(result) +
+                                                    " zahl: " + std::to_string(zahl) + " ziffer_vorgaenger = " + std::to_string(ziffer_vorgaenger) + " AnalogFehler = " +  std::to_string(AnalogFehler));
+        return result;
+    }
+
+    zahl_min = zahl - AnalogFehler / 10.0;
+    zahl_max = zahl + AnalogFehler / 10.0;
+
+    if ((int) floor(zahl_max) - (int) floor(zahl_min) != 0)
+    {
+        if (ziffer_vorgaenger <= AnalogFehler)
+        {
+            result = ((int) floor(zahl_max) + 10) % 10;
+            if (debugdetailgeneral) LogFile.WriteToFile("ClassFlowCNNGeneral::ZeigerEvalAnalogNeu - Zahl uneindeutig, Korrektur nach oben - Ergebnis = " + std::to_string(result) +
+                                                        " zahl: " + std::to_string(zahl) + " ziffer_vorgaenger = " + std::to_string(ziffer_vorgaenger) + " AnalogFehler = " +  std::to_string(AnalogFehler));
+            return result;
+        }
+        if (ziffer_vorgaenger >= 10 - AnalogFehler)
+        {
+            result = ((int) floor(zahl_min) + 10) % 10;
+            if (debugdetailgeneral) LogFile.WriteToFile("ClassFlowCNNGeneral::ZeigerEvalAnalogNeu - Zahl uneindeutig, Korrektur nach unten - Ergebnis = " + std::to_string(result) +
+                                                        " zahl: " + std::to_string(zahl) + " ziffer_vorgaenger = " + std::to_string(ziffer_vorgaenger) + " AnalogFehler = " +  std::to_string(AnalogFehler));
+            return result;
+        }
+    }
+    
+
+    result = ((int) floor(zahl) + 10) % 10;
+    if (debugdetailgeneral) LogFile.WriteToFile("ClassFlowCNNGeneral::ZeigerEvalAnalogNeu - Zahl eindeutig, keine Korrektur notwendig - Ergebnis = " + std::to_string(result) +
+                                                " zahl: " + std::to_string(zahl) + " ziffer_vorgaenger = " + std::to_string(ziffer_vorgaenger) + " AnalogFehler = " +  std::to_string(AnalogFehler));
+
+    return result;
+
+}
+
+/*
 int ClassFlowCNNGeneral::ZeigerEval(float zahl, int ziffer_vorgaenger)
 {   
     int ergebnis_nachkomma = ((int) floor(zahl * 10) + 10) % 10;
@@ -273,6 +370,7 @@ int ClassFlowCNNGeneral::ZeigerEval(float zahl, int ziffer_vorgaenger)
     ergebnis = (ergebnis + 10) % 10;
     return ergebnis;
 }
+*/
 
 bool ClassFlowCNNGeneral::ReadParameter(FILE* pfile, string& aktparamgraph)
 {

code/components/jomjol_flowcontroll/ClassFlowCNNGeneral.h

@@ -24,6 +24,13 @@ protected:
     t_CNNType CNNType;
     std::vector<general*> GENERAL;
     float CNNGoodThreshold;
+    float AnalogFehler = 3.0;
+    float AnalogToDigtalFehler = 0.8;
+    float DigitalUnschaerfe = 0.2;
+    int DigitalBand = 3;
+    float DigitalAnalogerVorgaengerUebergangsbereich = 2;
+    float DigitalUebergangsbereichVorgaengerAnalogToDigit = 1; // war vorher 2
+    float DigitalUebergangsbereichVorgaenger = 0.9;
 
     string cnnmodelfile;
     int modelxsize, modelysize, modelchannel;
@@ -34,8 +41,12 @@ protected:
     bool SaveAllFiles;   
 //    bool extendedResolution;
 
-    int ZeigerEval(float zahl, int ziffer_vorgaenger);
-    int ZeigerEvalHybrid(float zahl, float zahl_vorgaenger, int eval_vorgaenger);
+//    int ZeigerEval(float zahl, int ziffer_vorgaenger);
+//    int ZeigerEvalHybrid(float zahl, float zahl_vorgaenger, int eval_vorgaenger);
+    int ZeigerEvalAnalogNeu(float zahl, int ziffer_vorgaenger);
+    int ZeigerEvalAnalogToDigitNeu(float zahl, float ziffer_vorgaenger);
+    int ZeigerEvalHybridNeu(float zahl, float zahl_vorgaenger, int eval_vorgaenger, bool AnalogerVorgaenger = false);
+
 
 
     bool doNeuralNetwork(string time); 
@@ -50,7 +61,7 @@ public:
     bool doFlow(string time);
 
     string getHTMLSingleStep(string host);
-    string getReadout(int _analog, bool _extendedResolution = false, int prev = -1);   
+    string getReadout(int _analog, bool _extendedResolution = false, int prev = -1, float _vorgaengerAnalog = -1);   
 
     void DrawROI(CImageBasis *_zw); 
 

code/components/jomjol_flowcontroll/ClassFlowControll.cpp

@@ -305,6 +305,7 @@ bool ClassFlowControll::doFlow(string time)
             if (i) i -= 1;    // vorheriger Schritt muss wiederholt werden (vermutlich Bilder aufnehmen)
             result = false;
             if (repeat > 5) {
+                LogFile.SwitchOnOff(true);
                 LogFile.WriteToFile("Wiederholung 5x nicht erfolgreich --> reboot");
                 doReboot();
                 // Schritt wurde 5x wiederholt --> reboot
@@ -493,6 +494,8 @@ bool ClassFlowControll::ReadParameter(FILE* pfile, string& aktparamgraph)
                 // reboot notwendig damit die neue wlan.ini auch benutzt wird !!!
                 fclose(pfile);
                 printf("do reboot\n");
+                LogFile.SwitchOnOff(true);
+                LogFile.WriteToFile("Reboot to activate new HOSTNAME.");
                 esp_restart();
                 hard_restart();                   
                 doReboot();

code/components/jomjol_flowcontroll/ClassFlowPostProcessing.cpp

@@ -238,8 +238,9 @@ void ClassFlowPostProcessing::SavePreValue()
 
         _zw = NUMBERS[j]->name + "\t" + NUMBERS[j]->timeStamp + "\t" + RundeOutput(NUMBERS[j]->PreValue, NUMBERS[j]->Nachkomma) + "\n";
         printf("Write PreValue Zeile: %s\n", _zw.c_str());
-
-        fputs(_zw.c_str(), pFile);
+        if (pFile) {
+            fputs(_zw.c_str(), pFile);
+        }
     }
 
     UpdatePreValueINI = false;
@@ -568,8 +569,10 @@ void ClassFlowPostProcessing::InitNUMBERS()
         NUMBERS.push_back(_number);
     }
 
-    for (int i = 0; i < NUMBERS.size(); ++i)
+    for (int i = 0; i < NUMBERS.size(); ++i) {
         printf("Number %s, Anz DIG: %d, Anz ANA %d\n", NUMBERS[i]->name.c_str(), NUMBERS[i]->AnzahlDigital, NUMBERS[i]->AnzahlAnalog);
+    }
+
 }
 
 string ClassFlowPostProcessing::ShiftDecimal(string in, int _decShift){
@@ -667,7 +670,7 @@ bool ClassFlowPostProcessing::doFlow(string zwtime)
         if (NUMBERS[j]->digit_roi)
         {
             if (NUMBERS[j]->analog_roi) 
-                NUMBERS[j]->ReturnRawValue = flowDigit->getReadout(j, false, previous_value) + NUMBERS[j]->ReturnRawValue;
+                NUMBERS[j]->ReturnRawValue = flowDigit->getReadout(j, false, previous_value, NUMBERS[j]->analog_roi->ROI[0]->result_float) + NUMBERS[j]->ReturnRawValue;
             else
                 NUMBERS[j]->ReturnRawValue = flowDigit->getReadout(j, NUMBERS[j]->isExtendedResolution, previous_value);        // Extended Resolution nur falls es keine analogen Ziffern gibt
         }

code/components/jomjol_logfile/ClassLogFile.cpp

@@ -73,7 +73,7 @@ void ClassLogFile::WriteToDedicatedFile(std::string _fn, std::string info, bool
 
 //    pFile = OpenFileAndWait(_fn.c_str(), "a"); 
     pFile = fopen(_fn.c_str(), "a+");
-    printf("Logfile opened: %s\n", _fn.c_str());
+//    printf("Logfile opened: %s\n", _fn.c_str());
 
     if (pFile!=NULL) {
         if (_time)

code/components/tflite-lib/CMakeLists.txt

@@ -25,7 +25,8 @@ list(REMOVE_ITEM srcs_kernels
           "${tfmicro_kernels_dir}/depthwise_conv.cc"
           "${tfmicro_kernels_dir}/fully_connected.cc"
           "${tfmicro_kernels_dir}/mul.cc"
-          "${tfmicro_kernels_dir}/pooling.cc")
+          "${tfmicro_kernels_dir}/pooling.cc"
+          "${tfmicro_kernels_dir}/softmax.cc")
 
 FILE(GLOB esp_nn_kernels
           "${tfmicro_kernels_dir}/esp_nn/*.cc")
@@ -38,6 +39,10 @@ set(lib_srcs
           "${tflite_dir}/kernels/kernel_util.cc"
           "${tflite_dir}/micro/memory_planner/greedy_memory_planner.cc"
           "${tflite_dir}/micro/memory_planner/linear_memory_planner.cc"
+          "${tflite_dir}/micro/arena_allocator/non_persistent_arena_buffer_allocator.cc"
+          "${tflite_dir}/micro/arena_allocator/persistent_arena_buffer_allocator.cc"
+          "${tflite_dir}/micro/arena_allocator/recording_single_arena_buffer_allocator.cc"
+          "${tflite_dir}/micro/arena_allocator/single_arena_buffer_allocator.cc"
           "${tflite_dir}/c/common.cc"
           "${tflite_dir}/core/api/error_reporter.cc"
           "${tflite_dir}/core/api/flatbuffer_conversions.cc"

code/components/tflite-lib/tensorflow/lite/builtin_ops.h

@@ -179,6 +179,12 @@ typedef enum {
   kTfLiteBuiltinMultinomial = 149,
   kTfLiteBuiltinGelu = 150,
   kTfLiteBuiltinDynamicUpdateSlice = 151,
+  kTfLiteBuiltinRelu0To1 = 152,
+  kTfLiteBuiltinUnsortedSegmentProd = 153,
+  kTfLiteBuiltinUnsortedSegmentMax = 154,
+  kTfLiteBuiltinUnsortedSegmentSum = 155,
+  kTfLiteBuiltinAtan2 = 156,
+  kTfLiteBuiltinUnsortedSegmentMin = 157,
 } TfLiteBuiltinOperator;
 
 #ifdef __cplusplus

code/components/tflite-lib/tensorflow/lite/c/c_api_types.h

@@ -113,7 +113,13 @@ typedef struct TfLiteQuantizationParams {
 } TfLiteQuantizationParams;
 
 // --------------------------------------------------------------------------
-// Opaque types used by c_api_opaque.h.
+// Opaque types used by c_api.h, c_api_opaque.h and common.h.
+
+// TfLiteOpaqueContext is an opaque version of TfLiteContext;
+typedef struct TfLiteOpaqueContext TfLiteOpaqueContext;
+
+// TfLiteOpaqueNode is an opaque version of TfLiteNode;
+typedef struct TfLiteOpaqueNode TfLiteOpaqueNode;
 
 // TfLiteOpaqueTensor is an opaque version of TfLiteTensor;
 typedef struct TfLiteOpaqueTensor TfLiteOpaqueTensor;

code/components/tflite-lib/tensorflow/lite/c/common.cc

@@ -14,7 +14,11 @@ limitations under the License.
 ==============================================================================*/
 
 #include "tensorflow/lite/c/common.h"
+
 #include "tensorflow/lite/c/c_api_types.h"
+#ifdef TF_LITE_TENSORFLOW_PROFILER
+#include "tensorflow/lite/tensorflow_profiler_logger.h"
+#endif
 
 #ifndef TF_LITE_STATIC_MEMORY
 #include <stdlib.h>
@@ -99,7 +103,12 @@ void TfLiteFloatArrayFree(TfLiteFloatArray* a) { free(a); }
 void TfLiteTensorDataFree(TfLiteTensor* t) {
   if (t->allocation_type == kTfLiteDynamic ||
       t->allocation_type == kTfLitePersistentRo) {
-    free(t->data.raw);
+    if (t->data.raw) {
+#ifdef TF_LITE_TENSORFLOW_PROFILER
+      tflite::OnTfLiteTensorDealloc(t);
+#endif
+      free(t->data.raw);
+    }
   }
   t->data.raw = nullptr;
 }
@@ -161,7 +170,7 @@ void TfLiteTensorFree(TfLiteTensor* t) {
   t->dims = nullptr;
 
   if (t->dims_signature) {
-    TfLiteIntArrayFree((TfLiteIntArray *) t->dims_signature);
+    TfLiteIntArrayFree((TfLiteIntArray*)t->dims_signature);
   }
   t->dims_signature = nullptr;
 
@@ -191,16 +200,12 @@ void TfLiteTensorReset(TfLiteType type, const char* name, TfLiteIntArray* dims,
 }
 
 TfLiteStatus TfLiteTensorCopy(const TfLiteTensor* src, TfLiteTensor* dst) {
-  if (!src || !dst)
-    return kTfLiteOk;
-  if (src->bytes != dst->bytes)
-    return kTfLiteError;
-  if (src == dst)
-    return kTfLiteOk;
+  if (!src || !dst) return kTfLiteOk;
+  if (src->bytes != dst->bytes) return kTfLiteError;
+  if (src == dst) return kTfLiteOk;
 
   dst->type = src->type;
-  if (dst->dims)
-    TfLiteIntArrayFree(dst->dims);
+  if (dst->dims) TfLiteIntArrayFree(dst->dims);
   dst->dims = TfLiteIntArrayCopy(src->dims);
   memcpy(dst->data.raw, src->data.raw, src->bytes);
   dst->buffer_handle = src->buffer_handle;
@@ -218,8 +223,17 @@ void TfLiteTensorRealloc(size_t num_bytes, TfLiteTensor* tensor) {
   // TODO(b/145340303): Tensor data should be aligned.
   if (!tensor->data.raw) {
     tensor->data.raw = (char*)malloc(num_bytes);
+#ifdef TF_LITE_TENSORFLOW_PROFILER
+    tflite::OnTfLiteTensorAlloc(tensor, num_bytes);
+#endif
   } else if (num_bytes > tensor->bytes) {
+#ifdef TF_LITE_TENSORFLOW_PROFILER
+    tflite::OnTfLiteTensorDealloc(tensor);
+#endif
     tensor->data.raw = (char*)realloc(tensor->data.raw, num_bytes);
+#ifdef TF_LITE_TENSORFLOW_PROFILER
+    tflite::OnTfLiteTensorAlloc(tensor, num_bytes);
+#endif
   }
   tensor->bytes = num_bytes;
 }

code/components/tflite-lib/tensorflow/lite/c/common.h

@@ -173,9 +173,9 @@ void TfLiteFloatArrayFree(TfLiteFloatArray* a);
     }                                                 \
   } while (false)
 #else  // TF_LITE_STRIP_ERROR_STRINGS
-#define UNUSED(...) (void)sizeof(#__VA_ARGS__)
-#define TF_LITE_KERNEL_LOG(context, ...) UNUSED(__VA_ARGS__)
-#define TF_LITE_MAYBE_KERNEL_LOG(context, ...) UNUSED(__VA_ARGS__)
+#define ARGS_UNUSED(...) (void)sizeof(#__VA_ARGS__)
+#define TF_LITE_KERNEL_LOG(context, ...) ARGS_UNUSED(__VA_ARGS__)
+#define TF_LITE_MAYBE_KERNEL_LOG(context, ...) ARGS_UNUSED(__VA_ARGS__)
 #endif  // TF_LITE_STRIP_ERROR_STRINGS
 
 // Check whether value is true, and if not return kTfLiteError from
@@ -842,6 +842,12 @@ typedef struct TfLiteContext {
                                    size_t* bytes);
 } TfLiteContext;
 
+// `TfLiteRegistrationExternal` is an external version of `TfLiteRegistration`
+// for C API which doesn't use internal types (such as `TfLiteContext`) but only
+// uses stable API types (such as `TfLiteOpaqueContext`). The purpose of each
+// field is the exactly the same as with `TfLiteRegistration`.
+typedef struct TfLiteRegistrationExternal TfLiteRegistrationExternal;
+
 typedef struct TfLiteRegistration {
   // Initializes the op from serialized data.
   // Called only *once* for the lifetime of the op, so any one-time allocations
@@ -903,8 +909,31 @@ typedef struct TfLiteRegistration {
   // Note: It is the responsibility of the registration binder to set this
   // properly.
   int version;
+
+  // The external version of `TfLiteRegistration`. Since we can't use internal
+  // types (such as `TfLiteContext`) for C API to maintain ABI stability.
+  // C API user will provide `TfLiteRegistrationExternal` to implement custom
+  // ops. We keep it inside of `TfLiteRegistration` and use it to route
+  // callbacks properly.
+  TfLiteRegistrationExternal* registration_external;
 } TfLiteRegistration;
 
+// Old version of `TfLiteRegistration` to maintain binary backward
+// compatibility.
+// WARNING: This structure is deprecated / not an official part of the API.
+// It should be only used for binary backward compatibility.
+typedef struct TfLiteRegistration_V1 {
+  void* (*init)(TfLiteContext* context, const char* buffer, size_t length);
+  void (*free)(TfLiteContext* context, void* buffer);
+  TfLiteStatus (*prepare)(TfLiteContext* context, TfLiteNode* node);
+  TfLiteStatus (*invoke)(TfLiteContext* context, TfLiteNode* node);
+  const char* (*profiling_string)(const TfLiteContext* context,
+                                  const TfLiteNode* node);
+  int32_t builtin_code;
+  const char* custom_name;
+  int version;
+} TfLiteRegistration_V1;
+
 // The flags used in `TfLiteDelegate`. Note that this is a bitmask, so the
 // values should be 1, 2, 4, 8, ...etc.
 typedef enum TfLiteDelegateFlags {

code/components/tflite-lib/tensorflow/lite/core/api/flatbuffer_conversions.cc

@@ -493,6 +493,11 @@ TfLiteStatus ParseOpDataTfLite(const Operator* op, BuiltinOperator op_type,
       return ParseSquare(op, error_reporter, allocator, builtin_data);
     }
 
+    case BuiltinOperator_SQUARED_DIFFERENCE: {
+      return ParseSquaredDifference(op, error_reporter, allocator,
+                                    builtin_data);
+    }
+
     case BuiltinOperator_SQUEEZE: {
       return ParseSqueeze(op, error_reporter, allocator, builtin_data);
     }
@@ -840,14 +845,25 @@ TfLiteStatus ParseOpDataTfLite(const Operator* op, BuiltinOperator op_type,
     // TODO(aselle): Implement call in BuiltinOptions, but nullptrs are
     // ok for now, since there is no call implementation either.
     case BuiltinOperator_CALL:
+    case BuiltinOperator_COMPLEX_ABS:
     case BuiltinOperator_CONCAT_EMBEDDINGS:
     case BuiltinOperator_COS:
     case BuiltinOperator_CUSTOM:
+    case BuiltinOperator_DENSIFY:
+    case BuiltinOperator_DYNAMIC_UPDATE_SLICE:
     case BuiltinOperator_EMBEDDING_LOOKUP:
     case BuiltinOperator_EQUAL:
+    case BuiltinOperator_HASHTABLE_FIND:
+    case BuiltinOperator_HASHTABLE_IMPORT:
+    case BuiltinOperator_HASHTABLE_SIZE:
+    case BuiltinOperator_IMAG:
     case BuiltinOperator_MATRIX_DIAG:
     case BuiltinOperator_MATRIX_SET_DIAG:
+    case BuiltinOperator_NON_MAX_SUPPRESSION_V4:
+    case BuiltinOperator_NON_MAX_SUPPRESSION_V5:
     case BuiltinOperator_RELU_N1_TO_1:
+    case BuiltinOperator_RELU_0_TO_1:
+    case BuiltinOperator_SCATTER_ND:
     case BuiltinOperator_SELECT:
     case BuiltinOperator_SELECT_V2:
     case BuiltinOperator_SLICE:
@@ -855,23 +871,17 @@ TfLiteStatus ParseOpDataTfLite(const Operator* op, BuiltinOperator op_type,
     case BuiltinOperator_TOPK_V2:
     case BuiltinOperator_TRANSPOSE:
     case BuiltinOperator_RANGE:
-    case BuiltinOperator_SQUARED_DIFFERENCE:
-    case BuiltinOperator_REVERSE_V2:
-    case BuiltinOperator_WHERE:
     case BuiltinOperator_RANK:
-    case BuiltinOperator_NON_MAX_SUPPRESSION_V4:
-    case BuiltinOperator_NON_MAX_SUPPRESSION_V5:
-    case BuiltinOperator_SCATTER_ND:
-    case BuiltinOperator_DENSIFY:
-    case BuiltinOperator_SEGMENT_SUM:
-    case BuiltinOperator_RFFT2D:
-    case BuiltinOperator_IMAG:
     case BuiltinOperator_REAL:
-    case BuiltinOperator_COMPLEX_ABS:
-    case BuiltinOperator_HASHTABLE_FIND:
-    case BuiltinOperator_HASHTABLE_IMPORT:
-    case BuiltinOperator_HASHTABLE_SIZE:
-    case BuiltinOperator_DYNAMIC_UPDATE_SLICE:
+    case BuiltinOperator_RFFT2D:
+    case BuiltinOperator_SEGMENT_SUM:
+    case BuiltinOperator_REVERSE_V2:
+    case BuiltinOperator_UNSORTED_SEGMENT_MAX:
+    case BuiltinOperator_UNSORTED_SEGMENT_MIN:
+    case BuiltinOperator_UNSORTED_SEGMENT_PROD:
+    case BuiltinOperator_UNSORTED_SEGMENT_SUM:
+    case BuiltinOperator_ATAN2:
+    case BuiltinOperator_WHERE:
       return kTfLiteOk;
     case BuiltinOperator_PLACEHOLDER_FOR_GREATER_OP_CODES:
       return kTfLiteError;
@@ -2189,6 +2199,14 @@ TfLiteStatus ParseSquare(const Operator*, ErrorReporter*, BuiltinDataAllocator*,
   return kTfLiteOk;
 }
 
+// We have this parse function instead of directly returning kTfLiteOk from the
+// switch-case in ParseOpData because this function is used as part of the
+// selective registration for the OpResolver implementation in micro.
+TfLiteStatus ParseSquaredDifference(const Operator*, ErrorReporter*,
+                                    BuiltinDataAllocator*, void**) {
+  return kTfLiteOk;
+}
+
 TfLiteStatus ParseStridedSlice(const Operator* op,
                                ErrorReporter* error_reporter,
                                BuiltinDataAllocator* allocator,

code/components/tflite-lib/tensorflow/lite/core/api/flatbuffer_conversions.h

@@ -356,6 +356,11 @@ TfLiteStatus ParseSqrt(const Operator* op, ErrorReporter* error_reporter,
 TfLiteStatus ParseSquare(const Operator* op, ErrorReporter* error_reporter,
                          BuiltinDataAllocator* allocator, void** builtin_data);
 
+TfLiteStatus ParseSquaredDifference(const Operator* op,
+                                    ErrorReporter* error_reporter,
+                                    BuiltinDataAllocator* allocator,
+                                    void** builtin_data);
+
 TfLiteStatus ParseStridedSlice(const Operator* op,
                                ErrorReporter* error_reporter,
                                BuiltinDataAllocator* allocator,

code/components/tflite-lib/tensorflow/lite/core/api/op_resolver.h

@@ -23,6 +23,16 @@ limitations under the License.
 #include "tensorflow/lite/core/api/error_reporter.h"
 #include "tensorflow/lite/schema/schema_generated.h"
 
+// Opaque type similar to TfLiteDelegate / TfLiteOpaqueDelegate.
+// This is used for cases (e.g. when using "TF Lite with Google Play Services")
+// where the TF Lite runtime might be built using a newer (or older)
+// version of the TF Lite sources than the app, and hence might have a
+// different definition of the TfLiteDelegate type. TF Lite APIs use
+// TfLiteOpaqueDelegate rather than TfLiteDelegate when they want to
+// refer to a delegate defined with that potentially different version
+// of the TfLiteDelegate type.
+struct TfLiteOpaqueDelegateStruct;
+
 namespace tflite {
 
 /// Abstract interface that returns TfLiteRegistrations given op codes or custom
@@ -37,8 +47,10 @@ class OpResolver {
   virtual const TfLiteRegistration* FindOp(const char* op,
                                            int version) const = 0;
 
+  // Represents a sequence of delegates.
   using TfLiteDelegatePtrVector =
       std::vector<std::unique_ptr<TfLiteDelegate, void (*)(TfLiteDelegate*)>>;
+
   // Returns optional delegates for resolving and handling ops in the flatbuffer
   // model. This may be used in addition to the standard TfLiteRegistration
   // lookup for graph resolution.
@@ -47,16 +59,55 @@ class OpResolver {
     return {};
   }
 
-  // Represent a function that creates a TfLite delegate instance.
+  // Represents a function that creates a TfLite delegate instance.
   using TfLiteDelegateCreator =
       std::function<std::unique_ptr<TfLiteDelegate, void (*)(TfLiteDelegate*)>(
           int /*num_threads*/)>;
+
+  // Represents a sequence of delegate creator functions.
   using TfLiteDelegateCreators = std::vector<TfLiteDelegateCreator>;
+
   // Returns a vector of delegate creators to create optional delegates for
   // resolving and handling ops in the flatbuffer model. This may be used in
   // addition to the standard TfLiteRegistration lookup for graph resolution.
+  //
+  // Note that this method is not used (will not be called) if you are using
+  // TF Lite in Google Play Services; the GetOpaqueDelegateCreators method
+  // (see below) is used for that case.
   virtual TfLiteDelegateCreators GetDelegateCreators() const { return {}; }
 
+  // TODO(b/202712825): it would be nice if we could avoid the need for separate
+  // "opaque" types & methods for use only with TF Lite in Google Play Services.
+
+  // Represents an opaque delegate instance.
+  // WARNING: Experimental interface, subject to change.
+  using TfLiteOpaqueDelegatePtr =
+      std::unique_ptr<TfLiteOpaqueDelegateStruct,
+                      void (*)(TfLiteOpaqueDelegateStruct*)>;
+
+  // Represents a function that creates an opaque delegate instance.
+  // WARNING: Experimental interface, subject to change.
+  using TfLiteOpaqueDelegateCreator =
+      std::function<TfLiteOpaqueDelegatePtr(int /*num_threads*/)>;
+
+  // Represents a sequence of opaque delegate creator functions.
+  // WARNING: Experimental interface, subject to change.
+  using TfLiteOpaqueDelegateCreators = std::vector<TfLiteOpaqueDelegateCreator>;
+
+  // Returns a vector of opaque delegate creators to create optional opaque
+  // delegates for resolving and handling ops in the flatbuffer model. This may
+  // be used in addition to the standard TfLiteRegistration lookup for graph
+  // resolution.
+  //
+  // Note that this method will be called only if you are using TF Lite in
+  // Google Play Services; if you are using regular TF Lite, GetDelegateCreators
+  // (see above) is used instead.
+  //
+  // WARNING: Experimental interface, subject to change.
+  virtual TfLiteOpaqueDelegateCreators GetOpaqueDelegateCreators() const {
+    return {};
+  }
+
   virtual ~OpResolver() {}
 
  private:

code/components/tflite-lib/tensorflow/lite/experimental/microfrontend/lib/fft.cc

@@ -13,10 +13,10 @@ See the License for the specific language governing permissions and
 limitations under the License.
 ==============================================================================*/
 #include "tensorflow/lite/experimental/microfrontend/lib/fft.h"
-#include "tensorflow/lite/experimental/microfrontend/lib/kiss_fft_int16.h"
 
 #include <string.h>
 
+#include "tensorflow/lite/experimental/microfrontend/lib/kiss_fft_int16.h"
 
 void FftCompute(struct FftState* state, const int16_t* input,
                 int input_scale_shift) {
@@ -37,9 +37,9 @@ void FftCompute(struct FftState* state, const int16_t* input,
 
   // Apply the FFT.
   kissfft_fixed16::kiss_fftr(
-    reinterpret_cast<kissfft_fixed16::kiss_fftr_cfg>(state->scratch),
-    state->input,
-    reinterpret_cast<kissfft_fixed16::kiss_fft_cpx*>(state->output));
+      reinterpret_cast<kissfft_fixed16::kiss_fftr_cfg>(state->scratch),
+      state->input,
+      reinterpret_cast<kissfft_fixed16::kiss_fft_cpx*>(state->output));
 }
 
 void FftInit(struct FftState* state) {

code/components/tflite-lib/tensorflow/lite/experimental/microfrontend/lib/fft_util.cc

@@ -13,10 +13,11 @@ See the License for the specific language governing permissions and
 limitations under the License.
 ==============================================================================*/
 #include "tensorflow/lite/experimental/microfrontend/lib/fft_util.h"
-#include "tensorflow/lite/experimental/microfrontend/lib/kiss_fft_int16.h"
 
 #include <stdio.h>
 
+#include "tensorflow/lite/experimental/microfrontend/lib/kiss_fft_int16.h"
+
 int FftPopulateState(struct FftState* state, size_t input_size) {
   state->input_size = input_size;
   state->fft_size = 1;

code/components/tflite-lib/tensorflow/lite/experimental/microfrontend/lib/kiss_fft_int16.h

@@ -31,4 +31,3 @@ namespace kissfft_fixed16 {
 #undef KISS_FFT_H
 
 #endif  // TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_KISS_FFT_INT16_H_
-

code/components/tflite-lib/tensorflow/lite/kernels/internal/common.h

@@ -15,6 +15,7 @@ limitations under the License.
 #ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_COMMON_H_
 #define TENSORFLOW_LITE_KERNELS_INTERNAL_COMMON_H_
 
+#include <algorithm>
 #ifndef ALLOW_SLOW_GENERIC_DEPTHWISECONV_FALLBACK
 #ifdef GEMMLOWP_ALLOW_SLOW_SCALAR_FALLBACK
 #define ALLOW_SLOW_GENERIC_DEPTHWISECONV_FALLBACK

code/components/tflite-lib/tensorflow/lite/kernels/internal/compatibility.h

@@ -86,6 +86,16 @@ using int32 = std::int32_t;
 using uint32 = std::uint32_t;
 #endif  // !defined(TF_LITE_STATIC_MEMORY)
 
+// Allow for cross-compiler usage of function signatures - currently used for
+// specifying named RUY profiler regions in templated methods.
+#if defined(_MSC_VER)
+#define TFLITE_PRETTY_FUNCTION __FUNCSIG__
+#elif defined(__GNUC__)
+#define TFLITE_PRETTY_FUNCTION __PRETTY_FUNCTION__
+#else
+#define TFLITE_PRETTY_FUNCTION __func__
+#endif
+
 // TFLITE_DEPRECATED()
 //
 // Duplicated from absl/base/macros.h to avoid pulling in that library.

code/components/tflite-lib/tensorflow/lite/kernels/internal/portable_tensor_utils.h

@@ -324,7 +324,7 @@ void ApplySigmoidFloat(const int16_t* input, int32_t n_batch, int32_t n_input,
 //     - n_input: the size for input and output.
 //     - output:  the 16 bit output
 // The input is in Qm.15-m format and the output is in Q0.15 format.
-void ApplyTanh(int32_t integer_bits, const int16_t* input, int32_t n_batch,
+void ApplyTanh(int32_t intger_bits, const int16_t* input, int32_t n_batch,
                int32_t n_input, int16_t* output);
 
 // Apply Tanh to a quantized vector. Tbe internal calculation is in float.

code/components/tflite-lib/tensorflow/lite/kernels/internal/reference/add.h

@@ -15,6 +15,7 @@ limitations under the License.
 #ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_ADD_H_
 #define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_ADD_H_
 
+#include <algorithm>
 #include <type_traits>
 
 #include "fixedpoint/fixedpoint.h"

code/components/tflite-lib/tensorflow/lite/kernels/internal/reference/concatenation.h

@@ -16,6 +16,8 @@ limitations under the License.
 #ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_CONCATENATION_H_
 #define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_CONCATENATION_H_
 
+#include <algorithm>
+
 #include "tensorflow/lite/kernels/internal/common.h"
 #include "tensorflow/lite/kernels/internal/compatibility.h"
 #include "tensorflow/lite/kernels/internal/cppmath.h"

code/components/tflite-lib/tensorflow/lite/kernels/internal/reference/conv.h

@@ -15,6 +15,8 @@ limitations under the License.
 #ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_CONV_H_
 #define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_CONV_H_
 
+#include <algorithm>
+
 #include "tensorflow/lite/kernels/internal/common.h"
 #include "tensorflow/lite/kernels/internal/types.h"
 

code/components/tflite-lib/tensorflow/lite/kernels/internal/reference/div.h

@@ -0,0 +1,247 @@
+/* Copyright 2020 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_DIV_H_
+#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_DIV_H_
+
+#include <algorithm>
+
+#include "tensorflow/lite/kernels/internal/common.h"
+
+namespace tflite {
+
+namespace reference_ops {
+
+template <typename T>
+inline void DivCheckArithmeticParams(const ArithmeticParams& params) {
+  TFLITE_DCHECK_LE(params.quantized_activation_min,
+                   params.quantized_activation_max);
+  // Input offset is negative input zero point. Activation tensors are
+  // asymmetric quantized so they span the full int8 range.
+  constexpr int32_t max_value =
+      static_cast<int32_t>(std::numeric_limits<T>::max());
+  TFLITE_DCHECK_GE(params.input1_offset, -max_value);
+  TFLITE_DCHECK_LE(params.input1_offset, max_value);
+  TFLITE_DCHECK_GE(params.input2_offset, -max_value);
+  TFLITE_DCHECK_LE(params.input2_offset, max_value);
+  TFLITE_DCHECK_GE(params.output_offset, -max_value);
+  TFLITE_DCHECK_LE(params.output_offset, max_value);
+}
+
+// Element-wise div that can often be used for inner loop of broadcast Div as
+// well as the non-broadcast Div.
+template <typename T>
+inline void DivElementwise(int size, const ArithmeticParams& params,
+                           const T* input1_data, const T* input2_data,
+                           T* output_data) {
+  DivCheckArithmeticParams<T>(params);
+
+  for (int i = 0; i < size; ++i) {
+    int32_t input1_val = params.input1_offset + input1_data[i];
+    int32_t input2_val = params.input2_offset + input2_data[i];
+    TFLITE_DCHECK_NE(input2_val, 0);
+    if (input2_val < 0) {
+      // Invert signs to avoid a negative input2_val as input2_inv needs to be
+      // positive to be used as multiplier of MultiplyByQuantizedMultiplier.
+      input1_val = -input1_val;
+      input2_val = -input2_val;
+    }
+    int recip_shift;
+    const int32_t input2_inv = GetReciprocal(input2_val, 31, &recip_shift);
+    const int headroom = CountLeadingSignBits(input1_val);
+    const int32_t unscaled_quotient =
+        MultiplyByQuantizedMultiplierGreaterThanOne(input1_val, input2_inv,
+                                                    headroom);
+    const int total_shift = params.output_shift - recip_shift - headroom;
+    const int32_t unclamped_result =
+        params.output_offset +
+        MultiplyByQuantizedMultiplierSmallerThanOneExp(
+            unscaled_quotient, params.output_multiplier, total_shift);
+    const int32_t clamped_output =
+        std::min(params.quantized_activation_max,
+                 std::max(params.quantized_activation_min, unclamped_result));
+    output_data[i] = static_cast<T>(clamped_output);
+  }
+}
+
+inline void Div(const ArithmeticParams& params,
+                const RuntimeShape& input1_shape, const uint8_t* input1_data,
+                const RuntimeShape& input2_shape, const uint8_t* input2_data,
+                const RuntimeShape& output_shape, uint8_t* output_data) {
+  TFLITE_DCHECK_LE(params.quantized_activation_min,
+                   params.quantized_activation_max);
+  const int flat_size =
+      MatchingElementsSize(input1_shape, input2_shape, output_shape);
+
+  DivElementwise(flat_size, params, input1_data, input2_data, output_data);
+}
+
+inline void Div(const ArithmeticParams& params,
+                const RuntimeShape& input1_shape, const int8_t* input1_data,
+                const RuntimeShape& input2_shape, const int8_t* input2_data,
+                const RuntimeShape& output_shape, int8_t* output_data) {
+  TFLITE_DCHECK_LE(params.quantized_activation_min,
+                   params.quantized_activation_max);
+  const int flat_size =
+      MatchingElementsSize(input1_shape, input2_shape, output_shape);
+
+  DivElementwise(flat_size, params, input1_data, input2_data, output_data);
+}
+
+template <typename T, int N = 5>
+inline void BroadcastDivSlowQuantized(
+    const ArithmeticParams& params, const RuntimeShape& unextended_input1_shape,
+    const T* input1_data, const RuntimeShape& unextended_input2_shape,
+    const T* input2_data, const RuntimeShape& unextended_output_shape,
+    T* output_data) {
+  TFLITE_DCHECK_LE(unextended_input1_shape.DimensionsCount(), N);
+  TFLITE_DCHECK_LE(unextended_input2_shape.DimensionsCount(), N);
+  TFLITE_DCHECK_LE(unextended_output_shape.DimensionsCount(), N);
+
+  NdArrayDesc<N> desc1;
+  NdArrayDesc<N> desc2;
+  NdArrayDesc<N> output_desc;
+  NdArrayDescsForElementwiseBroadcast(unextended_input1_shape,
+                                      unextended_input2_shape, &desc1, &desc2);
+  CopyDimsToDesc(RuntimeShape::ExtendedShape(N, unextended_output_shape),
+                 &output_desc);
+
+  DivCheckArithmeticParams<T>(params);
+
+  auto div_func = [&](int indexes[N]) {
+    int32_t input1_val =
+        params.input1_offset + input1_data[SubscriptToIndex(desc1, indexes)];
+    int32_t input2_val =
+        params.input2_offset + input2_data[SubscriptToIndex(desc2, indexes)];
+    TFLITE_DCHECK_NE(input2_val, 0);
+    if (input2_val < 0) {
+      // Invert signs to avoid a negative input2_val as input2_inv needs to be
+      // positive to be used as multiplier of MultiplyByQuantizedMultiplier.
+      input1_val = -input1_val;
+      input2_val = -input2_val;
+    }
+    int recip_shift;
+    const int32_t input2_inv = GetReciprocal(input2_val, 31, &recip_shift);
+    const int headroom = CountLeadingSignBits(input1_val);
+    const int32_t unscaled_quotient =
+        MultiplyByQuantizedMultiplierGreaterThanOne(input1_val, input2_inv,
+                                                    headroom);
+    const int total_shift = params.output_shift - recip_shift - headroom;
+    const int32_t unclamped_result =
+        params.output_offset +
+        MultiplyByQuantizedMultiplierSmallerThanOneExp(
+            unscaled_quotient, params.output_multiplier, total_shift);
+    const int32_t clamped_output =
+        std::min(params.quantized_activation_max,
+                 std::max(params.quantized_activation_min, unclamped_result));
+    output_data[SubscriptToIndex(output_desc, indexes)] =
+        static_cast<T>(clamped_output);
+  };
+  NDOpsHelper<N>(output_desc, div_func);
+}
+
+template <int N = 5>
+inline void BroadcastDivSlow(const ArithmeticParams& params,
+                             const RuntimeShape& unextended_input1_shape,
+                             const uint8_t* input1_data,
+                             const RuntimeShape& unextended_input2_shape,
+                             const uint8_t* input2_data,
+                             const RuntimeShape& unextended_output_shape,
+                             uint8_t* output_data) {
+  BroadcastDivSlowQuantized<uint8_t, N>(
+      params, unextended_input1_shape, input1_data, unextended_input2_shape,
+      input2_data, unextended_output_shape, output_data);
+}
+
+template <int N = 5>
+inline void BroadcastDivSlow(const ArithmeticParams& params,
+                             const RuntimeShape& unextended_input1_shape,
+                             const int8_t* input1_data,
+                             const RuntimeShape& unextended_input2_shape,
+                             const int8_t* input2_data,
+                             const RuntimeShape& unextended_output_shape,
+                             int8_t* output_data) {
+  BroadcastDivSlowQuantized<int8_t, N>(
+      params, unextended_input1_shape, input1_data, unextended_input2_shape,
+      input2_data, unextended_output_shape, output_data);
+}
+
+// TODO(jiawen): We can implement BroadcastDiv on buffers of arbitrary
+// dimensionality if the runtime code does a single loop over one dimension
+// that handles broadcasting as the base case. The code generator would then
+// generate max(D1, D2) nested for loops.
+template <typename T, int N = 5>
+void BroadcastDivSlow(const ArithmeticParams& params,
+                      const RuntimeShape& unextended_input1_shape,
+                      const T* input1_data,
+                      const RuntimeShape& unextended_input2_shape,
+                      const T* input2_data,
+                      const RuntimeShape& unextended_output_shape,
+                      T* output_data) {
+  T output_activation_min;
+  T output_activation_max;
+  GetActivationParams(params, &output_activation_min, &output_activation_max);
+
+  TFLITE_DCHECK_LE(unextended_input1_shape.DimensionsCount(), N);
+  TFLITE_DCHECK_LE(unextended_input2_shape.DimensionsCount(), N);
+  TFLITE_DCHECK_LE(unextended_output_shape.DimensionsCount(), N);
+
+  NdArrayDesc<N> desc1;
+  NdArrayDesc<N> desc2;
+  NdArrayDesc<N> output_desc;
+  NdArrayDescsForElementwiseBroadcast(unextended_input1_shape,
+                                      unextended_input2_shape, &desc1, &desc2);
+  CopyDimsToDesc(RuntimeShape::ExtendedShape(N, unextended_output_shape),
+                 &output_desc);
+
+  // In Tensorflow, the dimensions are canonically named (batch_number, row,
+  // col, channel), with extents (batches, height, width, depth), with the
+  // trailing dimension changing most rapidly (channels has the smallest
+  // stride, typically 1 element).
+  //
+  // In generated C code, we store arrays with the dimensions reversed. The
+  // first dimension has smallest stride.
+
+  auto div_func = [&](int indexes[N]) {
+    output_data[SubscriptToIndex(output_desc, indexes)] =
+        ActivationFunctionWithMinMax(
+            input1_data[SubscriptToIndex(desc1, indexes)] /
+                input2_data[SubscriptToIndex(desc2, indexes)],
+            output_activation_min, output_activation_max);
+  };
+  NDOpsHelper<N>(output_desc, div_func);
+}
+
+template <typename T>
+inline void Div(const ArithmeticParams& params,
+                const RuntimeShape& input1_shape, const T* input1_data,
+                const RuntimeShape& input2_shape, const T* input2_data,
+                const RuntimeShape& output_shape, T* output_data) {
+  T output_activation_min;
+  T output_activation_max;
+  GetActivationParams(params, &output_activation_min, &output_activation_max);
+
+  const int flat_size =
+      MatchingElementsSize(input1_shape, input2_shape, output_shape);
+  for (int i = 0; i < flat_size; ++i) {
+    output_data[i] = ActivationFunctionWithMinMax(
+        input1_data[i] / input2_data[i], output_activation_min,
+        output_activation_max);
+  }
+}
+
+}  // namespace reference_ops
+}  // namespace tflite
+
+#endif  // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_DIV_H_

code/components/tflite-lib/tensorflow/lite/kernels/internal/reference/fully_connected.h

@@ -15,6 +15,8 @@ limitations under the License.
 #ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_FULLY_CONNECTED_H_
 #define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_FULLY_CONNECTED_H_
 
+#include <algorithm>
+
 #include "ruy/profiler/instrumentation.h"  // from @ruy
 #include "tensorflow/lite/kernels/internal/common.h"
 #include "tensorflow/lite/kernels/internal/cppmath.h"

code/components/tflite-lib/tensorflow/lite/kernels/internal/reference/hard_swish.h

@@ -15,6 +15,8 @@ limitations under the License.
 #ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_ACTIVATIONS_H_
 #define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_ACTIVATIONS_H_
 
+#include <algorithm>
+
 #include "ruy/profiler/instrumentation.h"  // from @ruy
 #include "tensorflow/lite/kernels/internal/common.h"
 #include "tensorflow/lite/kernels/internal/types.h"
@@ -23,9 +25,9 @@ namespace tflite {
 namespace reference_ops {
 
 inline int16_t SaturatingLeftShift(int16_t value, int amount) {
-  int32_t result = static_cast<int32_t>(value) * (1 << amount);
-  result = std::min<int32_t>(result, std::numeric_limits<int16_t>::max());
-  result = std::max<int32_t>(result, std::numeric_limits<int16_t>::min());
+  int64_t result = static_cast<int64_t>(value) * (1 << amount);
+  result = std::min<int64_t>(result, std::numeric_limits<int16_t>::max());
+  result = std::max<int64_t>(result, std::numeric_limits<int16_t>::min());
   return result;
 }
 

code/components/tflite-lib/tensorflow/lite/kernels/internal/reference/integer_ops/add.h

@@ -15,6 +15,7 @@ limitations under the License.
 #ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_ADD_H_
 #define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_ADD_H_
 
+#include <algorithm>
 #include <limits>
 
 #include "tensorflow/lite/kernels/internal/common.h"

code/components/tflite-lib/tensorflow/lite/kernels/internal/reference/integer_ops/conv.h

@@ -15,6 +15,8 @@ limitations under the License.
 #ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_CONV_H_
 #define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_CONV_H_
 
+#include <algorithm>
+
 #include "tensorflow/lite/kernels/internal/common.h"
 
 namespace tflite {

code/components/tflite-lib/tensorflow/lite/kernels/internal/reference/integer_ops/depthwise_conv.h

@@ -15,6 +15,8 @@ limitations under the License.
 #ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_DEPTHWISE_CONV_H_
 #define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_DEPTHWISE_CONV_H_
 
+#include <algorithm>
+
 #include "tensorflow/lite/kernels/internal/common.h"
 
 namespace tflite {

code/components/tflite-lib/tensorflow/lite/kernels/internal/reference/integer_ops/fully_connected.h

@@ -15,11 +15,101 @@ limitations under the License.
 #ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_FULLY_CONNECTED_H_
 #define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_FULLY_CONNECTED_H_
 
+#include <algorithm>
+
 #include "tensorflow/lite/kernels/internal/common.h"
 
 namespace tflite {
 namespace reference_integer_ops {
 
+// For per-channel functions, since it is defined in quantization spec that
+// weights are symmetric
+// (https://www.tensorflow.org/lite/performance/quantization_spec#symmetric_vs_asymmetric),
+// zero_point (params.weights_offset) is always 0.
+// However, for per-tensor functions, params.weights_offset is still applied for
+// backward compatibility.
+
+inline void FullyConnectedPerChannel(
+    const FullyConnectedParams& params, const int32_t* output_multiplier,
+    const int* output_shift, const RuntimeShape& input_shape,
+    const int8_t* input_data, const RuntimeShape& filter_shape,
+    const int8_t* filter_data, const RuntimeShape& bias_shape,
+    const int32_t* bias_data, const RuntimeShape& output_shape,
+    int8_t* output_data) {
+  const int32_t input_offset = params.input_offset;
+  const int32_t output_offset = params.output_offset;
+  const int32_t output_activation_min = params.quantized_activation_min;
+  const int32_t output_activation_max = params.quantized_activation_max;
+  TFLITE_DCHECK_GE(filter_shape.DimensionsCount(), 2);
+  TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 2);
+
+  TFLITE_DCHECK_LE(output_activation_min, output_activation_max);
+  const int filter_dim_count = filter_shape.DimensionsCount();
+  const int batches = output_shape.Dims(0);
+  const int output_depth = output_shape.Dims(1);
+  TFLITE_DCHECK_LE(output_depth, filter_shape.Dims(filter_dim_count - 2));
+  const int accum_depth = filter_shape.Dims(filter_dim_count - 1);
+  for (int b = 0; b < batches; ++b) {
+    for (int out_c = 0; out_c < output_depth; ++out_c) {
+      int32_t acc = 0;
+      for (int d = 0; d < accum_depth; ++d) {
+        int32_t input_val = input_data[b * accum_depth + d];
+        int32_t filter_val = filter_data[out_c * accum_depth + d];
+        acc += filter_val * (input_val + input_offset);
+      }
+      if (bias_data) {
+        acc += bias_data[out_c];
+      }
+      acc = MultiplyByQuantizedMultiplier(acc, output_multiplier[out_c],
+                                          output_shift[out_c]);
+      acc += output_offset;
+      acc = std::max(acc, output_activation_min);
+      acc = std::min(acc, output_activation_max);
+      output_data[out_c + output_depth * b] = static_cast<int8_t>(acc);
+    }
+  }
+}
+
+template <typename AccumScalar>
+inline void FullyConnectedPerChannel(
+    const FullyConnectedParams& params, const int32_t* output_multiplier,
+    const int* output_shift, const RuntimeShape& input_shape,
+    const int16_t* input_data, const RuntimeShape& filter_shape,
+    const int8_t* filter_data, const RuntimeShape& bias_shape,
+    const AccumScalar* bias_data, const RuntimeShape& output_shape,
+    int16_t* output_data) {
+  const int32_t output_activation_min = params.quantized_activation_min;
+  const int32_t output_activation_max = params.quantized_activation_max;
+  TFLITE_DCHECK_GE(filter_shape.DimensionsCount(), 2);
+  TFLITE_DCHECK_GE(output_shape.DimensionsCount(), 1);
+
+  TFLITE_DCHECK_LE(output_activation_min, output_activation_max);
+  const int filter_dim_count = filter_shape.DimensionsCount();
+  const int output_dim_count = output_shape.DimensionsCount();
+  const int batches = FlatSizeSkipDim(output_shape, output_dim_count - 1);
+  const int output_depth = output_shape.Dims(output_dim_count - 1);
+  TFLITE_DCHECK_LE(output_depth, filter_shape.Dims(filter_dim_count - 2));
+  const int accum_depth = filter_shape.Dims(filter_dim_count - 1);
+  for (int b = 0; b < batches; ++b) {
+    for (int out_c = 0; out_c < output_depth; ++out_c) {
+      AccumScalar acc = 0;
+      for (int d = 0; d < accum_depth; ++d) {
+        int32_t input_val = input_data[b * accum_depth + d];
+        int32_t filter_val = filter_data[out_c * accum_depth + d];
+        acc += filter_val * input_val;
+      }
+      if (bias_data) {
+        acc += bias_data[out_c];
+      }
+      int32_t acc_scaled = MultiplyByQuantizedMultiplier(
+          acc, output_multiplier[out_c], output_shift[out_c]);
+      acc_scaled = std::max(acc_scaled, output_activation_min);
+      acc_scaled = std::min(acc_scaled, output_activation_max);
+      output_data[out_c + output_depth * b] = static_cast<int16_t>(acc_scaled);
+    }
+  }
+}
+
 inline void FullyConnected(
     const FullyConnectedParams& params, const RuntimeShape& input_shape,
     const int8_t* input_data, const RuntimeShape& filter_shape,

code/components/tflite-lib/tensorflow/lite/kernels/internal/reference/integer_ops/l2normalization.h

@@ -15,6 +15,8 @@ limitations under the License.
 #ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_L2NORMALIZATION_H_
 #define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_L2NORMALIZATION_H_
 
+#include <algorithm>
+
 #include "tensorflow/lite/kernels/internal/common.h"
 
 namespace tflite {

code/components/tflite-lib/tensorflow/lite/kernels/internal/reference/integer_ops/logistic.h

@@ -15,7 +15,9 @@ limitations under the License.
 #ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_LOGISTIC_H_
 #define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_LOGISTIC_H_
 
+#include <algorithm>
 #include <limits>
+
 #include "tensorflow/lite/kernels/internal/common.h"
 
 namespace tflite {

code/components/tflite-lib/tensorflow/lite/kernels/internal/reference/integer_ops/mean.h

@@ -15,6 +15,8 @@ limitations under the License.
 #ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_MEAN_H_
 #define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_MEAN_H_
 
+#include <algorithm>
+
 #include "tensorflow/lite/kernels/internal/common.h"
 
 namespace tflite {

code/components/tflite-lib/tensorflow/lite/kernels/internal/reference/integer_ops/mul.h

@@ -15,6 +15,8 @@ limitations under the License.
 #ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_MUL_H_
 #define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_MUL_H_
 
+#include <algorithm>
+
 #include "fixedpoint/fixedpoint.h"
 #include "ruy/profiler/instrumentation.h"  // from @ruy
 #include "tensorflow/lite/kernels/internal/common.h"

code/components/tflite-lib/tensorflow/lite/kernels/internal/reference/integer_ops/pooling.h

@@ -15,7 +15,9 @@ limitations under the License.
 #ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_POOLING_H_
 #define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_POOLING_H_
 
+#include <algorithm>
 #include <limits>
+
 #include "tensorflow/lite/kernels/internal/common.h"
 
 namespace tflite {

code/components/tflite-lib/tensorflow/lite/kernels/internal/reference/integer_ops/tanh.h

@@ -15,6 +15,7 @@ limitations under the License.
 #ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_TANH_H_
 #define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_TANH_H_
 
+#include <algorithm>
 #include <limits>
 
 #include "fixedpoint/fixedpoint.h"

code/components/tflite-lib/tensorflow/lite/kernels/internal/reference/integer_ops/transpose_conv.h

@@ -15,6 +15,8 @@ limitations under the License.
 #ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_TRANSPOSE_CONV_H_
 #define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_TRANSPOSE_CONV_H_
 
+#include <algorithm>
+
 #include "tensorflow/lite/kernels/internal/common.h"
 
 namespace tflite {

code/components/tflite-lib/tensorflow/lite/kernels/internal/reference/mul.h

@@ -15,6 +15,8 @@ limitations under the License.
 #ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_MUL_H_
 #define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_MUL_H_
 
+#include <algorithm>
+
 #include "tensorflow/lite/kernels/internal/common.h"
 
 namespace tflite {

code/components/tflite-lib/tensorflow/lite/kernels/internal/reference/pooling.h

@@ -15,6 +15,8 @@ limitations under the License.
 #ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_POOLING_H_
 #define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_POOLING_H_
 
+#include <algorithm>
+
 #include "tensorflow/lite/kernels/internal/common.h"
 #include "tensorflow/lite/kernels/internal/cppmath.h"
 #include "tensorflow/lite/kernels/internal/quantization_util.h"

code/components/tflite-lib/tensorflow/lite/kernels/internal/reference/prelu.h

@@ -15,6 +15,8 @@ limitations under the License.
 #ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_PRELU_H_
 #define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_PRELU_H_
 
+#include <algorithm>
+
 #include "tensorflow/lite/kernels/internal/common.h"
 #include "tensorflow/lite/kernels/internal/compatibility.h"
 #include "tensorflow/lite/kernels/internal/types.h"

code/components/tflite-lib/tensorflow/lite/kernels/internal/reference/process_broadcast_shapes.h

@@ -15,6 +15,8 @@ limitations under the License.
 #ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_PROCESS_BROADCAST_SHAPES_H_
 #define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_PROCESS_BROADCAST_SHAPES_H_
 
+#include <algorithm>
+
 #include "tensorflow/lite/kernels/internal/types.h"
 
 namespace tflite {

code/components/tflite-lib/tensorflow/lite/kernels/internal/reference/reduce.h

@@ -15,6 +15,8 @@ limitations under the License.
 #ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_REDUCE_H_
 #define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_REDUCE_H_
 
+#include <algorithm>
+
 #include "ruy/profiler/instrumentation.h"  // from @ruy
 #include "tensorflow/lite/kernels/internal/common.h"
 #include "tensorflow/lite/kernels/internal/cppmath.h"

code/components/tflite-lib/tensorflow/lite/kernels/internal/reference/requantize.h

@@ -15,6 +15,8 @@ limitations under the License.
 #ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_REQUANTIZE_H_
 #define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_REQUANTIZE_H_
 
+#include <algorithm>
+
 #include "ruy/profiler/instrumentation.h"  // from @ruy
 #include "tensorflow/lite/kernels/internal/common.h"
 #include "tensorflow/lite/kernels/internal/types.h"

code/components/tflite-lib/tensorflow/lite/kernels/internal/reference/resize_nearest_neighbor.h

@@ -15,6 +15,7 @@ limitations under the License.
 #ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_RESIZE_NEAREST_NEIGHBOR_H_
 #define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_RESIZE_NEAREST_NEIGHBOR_H_
 
+#include <algorithm>
 #include <cmath>
 
 #include "tensorflow/lite/kernels/internal/cppmath.h"

code/components/tflite-lib/tensorflow/lite/kernels/internal/reference/softmax.h

@@ -15,6 +15,7 @@ limitations under the License.
 #ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_SOFTMAX_H_
 #define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_SOFTMAX_H_
 
+#include <algorithm>
 #include <limits>
 
 #include "fixedpoint/fixedpoint.h"

code/components/tflite-lib/tensorflow/lite/kernels/internal/reference/transpose_conv.h

@@ -15,6 +15,8 @@ limitations under the License.
 #ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_TRANSPOSE_CONV_H_
 #define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_TRANSPOSE_CONV_H_
 
+#include <algorithm>
+
 #include "tensorflow/lite/kernels/internal/common.h"
 #include "tensorflow/lite/kernels/internal/types.h"
 

code/components/tflite-lib/tensorflow/lite/kernels/internal/runtime_shape.h

@@ -27,6 +27,11 @@ class RuntimeShape {
  public:
   RuntimeShape& operator=(RuntimeShape const&) = delete;
 
+  // RuntimeShape in TFLM supports up to 5 dimensions.
+  // The name kMaxSmallSize comes from the same file of the upstream
+  // tensorflow lite repo and need to be kept the same for max reuse.
+  static constexpr int kMaxSmallSize = 5;
+
   RuntimeShape() : size_(0) {}
 
   explicit RuntimeShape(int dimensions_count) : size_(dimensions_count) {}
@@ -104,11 +109,9 @@ class RuntimeShape {
                 sizeof(int32_t) * shape.DimensionsCount());
   }
 
-  // A maximum of 4 dimensions are supported on TFLM.
-  static constexpr int kMaxSize = 5;
   int32_t size_;
   union {
-    int32_t dims_[kMaxSize];
+    int32_t dims_[kMaxSmallSize];
   };
 };
 

code/components/tflite-lib/tensorflow/lite/kernels/internal/types.h

@@ -974,11 +974,11 @@ struct StridedSliceParams {
   int8_t strides_count;
   int32_t strides[5];
 
-  int16_t begin_mask;
-  int16_t ellipsis_mask;
-  int16_t end_mask;
-  int16_t new_axis_mask;
-  int16_t shrink_axis_mask;
+  uint16_t begin_mask;
+  uint16_t ellipsis_mask;
+  uint16_t end_mask;
+  uint16_t new_axis_mask;
+  uint16_t shrink_axis_mask;
 };
 
 struct TanhParams {

code/components/tflite-lib/tensorflow/lite/kernels/kernel_util.h

@@ -177,6 +177,14 @@ inline int64_t NumElements(const TfLiteTensor* t) {
   return NumElements(t->dims);
 }
 
+inline int64_t NumElements(const int* dims, int num_dims) {
+  int64_t count = 1;
+  for (int i = 0; i < num_dims; ++i) {
+    count *= dims[i];
+  }
+  return count;
+}
+
 // Determines whether tensor is constant.
 // TODO(b/138199592): Introduce new query which checks for constant OR
 // persistent-read-only, which would be useful for most tensor kernels that
@@ -308,7 +316,7 @@ TfLiteStatus CalculateShapeForBroadcast(TfLiteContext* context,
                                         const TfLiteTensor* input3,
                                         TfLiteIntArray** output_shape);
 
-// Return the size of given type in bytes. Return 0 in in case of string.
+// Return the size of given type in bytes. Return 0 in case of string.
 int TfLiteTypeGetSize(TfLiteType type);
 
 // Whether the current platform is mobile (Android or iOS).

code/components/tflite-lib/tensorflow/lite/micro/all_ops_resolver.cc

@@ -43,6 +43,7 @@ AllOpsResolver::AllOpsResolver() {
   AddDepthwiseConv2D();
   AddDequantize();
   AddDetectionPostprocess();
+  AddDiv();
   AddElu();
   AddEqual();
   AddEthosU();
@@ -104,6 +105,7 @@ AllOpsResolver::AllOpsResolver() {
   AddSqueeze();
   AddStridedSlice();
   AddSub();
+  AddSum();
   AddSvdf();
   AddTanh();
   AddTranspose();

code/components/tflite-lib/tensorflow/lite/micro/arena_allocator/ibuffer_allocator.h

@@ -12,8 +12,8 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 See the License for the specific language governing permissions and
 limitations under the License.
 ==============================================================================*/
-#ifndef TENSORFLOW_LITE_MICRO_IBUFFER_ALLOCATOR_H_
-#define TENSORFLOW_LITE_MICRO_IBUFFER_ALLOCATOR_H_
+#ifndef TENSORFLOW_LITE_MICRO_ARENA_ALLOCATOR_IBUFFER_ALLOCATOR_H_
+#define TENSORFLOW_LITE_MICRO_ARENA_ALLOCATOR_IBUFFER_ALLOCATOR_H_
 
 #include <cstddef>
 #include <cstdint>
@@ -97,4 +97,4 @@ class INonPersistentBufferAllocator {
 
 }  // namespace tflite
 
-#endif  // TENSORFLOW_LITE_MICRO_IBUFFER_ALLOCATOR_H_
+#endif  // TENSORFLOW_LITE_MICRO_ARENA_ALLOCATOR_IBUFFER_ALLOCATOR_H_

code/components/tflite-lib/tensorflow/lite/micro/arena_allocator/non_persistent_arena_buffer_allocator.cc

@@ -0,0 +1,170 @@
+/* Copyright 2022 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+#include "tensorflow/lite/micro/arena_allocator/non_persistent_arena_buffer_allocator.h"
+
+#include "tensorflow/lite/micro/memory_helpers.h"
+#include "tensorflow/lite/micro/micro_error_reporter.h"
+
+namespace tflite {
+
+NonPersistentArenaBufferAllocator::NonPersistentArenaBufferAllocator(
+    uint8_t* buffer, size_t buffer_size)
+    : buffer_head_(buffer),
+      buffer_tail_(buffer + buffer_size),
+      head_temp_(buffer),
+      next_temp_(buffer) {}
+
+NonPersistentArenaBufferAllocator::~NonPersistentArenaBufferAllocator() {}
+
+// Allocates a temporary buffer. This buffer is not resizable.
+uint8_t* NonPersistentArenaBufferAllocator::AllocateTemp(size_t size,
+                                                         size_t alignment) {
+  uint8_t* const aligned_result = AlignPointerUp(next_temp_, alignment);
+  const size_t available_memory = buffer_tail_ - aligned_result;
+  if (available_memory < size) {
+    MicroPrintf(
+        "Failed to allocate temp memory. Requested: %u, "
+        "available %u, missing: %u",
+        size, available_memory, size - available_memory);
+    return nullptr;
+  }
+  next_temp_ = aligned_result + size;
+  temp_buffer_ptr_check_sum_ ^= reinterpret_cast<intptr_t>(aligned_result);
+  temp_buffer_count_++;
+  return aligned_result;
+}
+
+// Signals that a temporary buffer is no longer needed.
+void NonPersistentArenaBufferAllocator::DeallocateTemp(uint8_t* temp_buf) {
+  temp_buffer_ptr_check_sum_ ^= reinterpret_cast<intptr_t>(temp_buf);
+  temp_buffer_count_--;
+}
+
+// Returns true if all temporary buffers are already deallocated.
+bool NonPersistentArenaBufferAllocator::IsAllTempDeallocated() {
+  if (temp_buffer_count_ != 0 || temp_buffer_ptr_check_sum_ != 0) {
+    MicroPrintf(
+        "Number of allocated temp buffers: %d. Checksum passing status: %d",
+        temp_buffer_count_, !temp_buffer_ptr_check_sum_);
+    return false;
+  }
+  return true;
+}
+
+// Signals that all temporary allocations can be reclaimed. TFLM calls this
+// API when it knows that all temporary buffers that it requested has been
+// deallocated. The goal of API is to facilitate implementations of
+// INonPersistentBufferAllocator can reuse buffer with some reasonable
+// complexity.
+TfLiteStatus NonPersistentArenaBufferAllocator::ResetTempAllocations() {
+  if (!IsAllTempDeallocated()) {
+    MicroPrintf(
+        "All temp buffers must be freed before calling ResetTempAllocations()");
+    return kTfLiteError;
+  }
+  next_temp_ = head_temp_;
+  return kTfLiteOk;
+}
+
+// Returns a buffer that is resizable viable ResizeBuffer().
+uint8_t* NonPersistentArenaBufferAllocator::AllocateResizableBuffer(
+    size_t size, size_t alignment) {
+  // Only supports one resizable buffer, which starts at the buffer head.
+  uint8_t* expected_resizable_buf = AlignPointerUp(buffer_head_, alignment);
+
+  if (resizable_buffer_allocated_) {
+    MicroPrintf(
+        "Cannot allocate a new resizable buffer when one is already allocated");
+    return nullptr;
+  }
+
+  if (ResizeBuffer(expected_resizable_buf, size, alignment) == kTfLiteOk) {
+    resizable_buffer_allocated_ = true;
+    return expected_resizable_buf;
+  }
+  return nullptr;
+}
+
+// Resizes a buffer that is previously returned by the AllocateResizableBuffer.
+// Note that ResizeBuffer(old_resizable_buf, 0, 1) effectively deallocates
+// a previous allocated resizable buffer.
+TfLiteStatus NonPersistentArenaBufferAllocator::ResizeBuffer(
+    uint8_t* resizable_buf, size_t size, size_t alignment) {
+  // Only supports one resizable buffer, which starts at the buffer head.
+  uint8_t* expect_resizable_buf = AlignPointerUp(buffer_head_, alignment);
+  if (resizable_buf != expect_resizable_buf) {
+    MicroPrintf("Internal error: buffer is not resizable");
+    return kTfLiteError;
+  }
+  if (head_temp_ != next_temp_) {
+    MicroPrintf("ResetTempAllocations() is not called before ResizeBuffer().");
+    return kTfLiteError;
+  }
+
+  const size_t available_memory = buffer_tail_ - expect_resizable_buf;
+  if (available_memory < size) {
+    MicroPrintf(
+        "Failed to resize buffer. Requested: %u, available %u, missing: %u",
+        size, available_memory, size - available_memory);
+    return kTfLiteError;
+  }
+  head_temp_ = expect_resizable_buf + size;
+  next_temp_ = head_temp_;
+
+  return kTfLiteOk;
+}
+
+// Frees up the memory occupied by the resizable buffer.
+TfLiteStatus NonPersistentArenaBufferAllocator::DeallocateResizableBuffer(
+    uint8_t* resizable_buf) {
+  TfLiteStatus status = ResizeBuffer(resizable_buf, 0, 1);
+  if (status == kTfLiteOk) {
+    resizable_buffer_allocated_ = false;
+  }
+  return status;
+}
+
+// Returns a pointer pointing to the start of the overlay memory, which is
+// used for activation tensors and scratch buffers by kernels at Invoke stage.
+uint8_t* NonPersistentArenaBufferAllocator::GetOverlayMemoryAddress() const {
+  return buffer_head_;
+}
+
+// Reserves the size of the overlay memory. This overlay is reserved for the
+// kernels at Invoke stage. This is referred to as the overlay because before
+// Invoket state, the same memory can be used for temp buffers. The layout of
+// the memory is planned by the memory planner separately at Invoke stage.
+TfLiteStatus
+NonPersistentArenaBufferAllocator::ReserveNonPersistentOverlayMemory(
+    size_t size, size_t alignment) {
+  uint8_t* expect_resizable_buf = AlignPointerUp(buffer_head_, alignment);
+  return ResizeBuffer(expect_resizable_buf, size, alignment);
+}
+
+// Returns the size of non-persistent buffer in use.
+size_t NonPersistentArenaBufferAllocator::GetNonPersistentUsedBytes() const {
+  return (next_temp_ - buffer_head_);
+}
+
+// Returns the number of bytes available with a given alignment. This number
+// takes in account any temporary allocations.
+size_t NonPersistentArenaBufferAllocator::GetAvailableMemory(
+    size_t alignment) const {
+  uint8_t* const aligned_temp = AlignPointerUp(next_temp_, alignment);
+  uint8_t* const aligned_tail = AlignPointerDown(buffer_tail_, alignment);
+  return aligned_tail - aligned_temp;
+}
+
+}  // namespace tflite

code/components/tflite-lib/tensorflow/lite/micro/arena_allocator/non_persistent_arena_buffer_allocator.h

@@ -0,0 +1,105 @@
+/* Copyright 2022 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+#ifndef TENSORFLOW_LITE_MICRO_ARENA_ALLOCATOR_NON_PERSISTENT_ARENA_BUFFER_ALLOCATOR_H_
+#define TENSORFLOW_LITE_MICRO_ARENA_ALLOCATOR_NON_PERSISTENT_ARENA_BUFFER_ALLOCATOR_H_
+
+#include <cstddef>
+#include <cstdint>
+
+#include "tensorflow/lite/c/common.h"
+#include "tensorflow/lite/core/api/error_reporter.h"
+#include "tensorflow/lite/micro/arena_allocator/ibuffer_allocator.h"
+#include "tensorflow/lite/micro/compatibility.h"
+
+namespace tflite {
+
+// Implement INonPersistentBufferAllocator on an arena that is dedicated for
+// non-persistent buffers.
+class NonPersistentArenaBufferAllocator : public INonPersistentBufferAllocator {
+ public:
+  NonPersistentArenaBufferAllocator(uint8_t* buffer, size_t buffer_size);
+  virtual ~NonPersistentArenaBufferAllocator();
+
+  // Allocates a temporary buffer. This buffer is not resizable.
+  uint8_t* AllocateTemp(size_t size, size_t alignment) override;
+
+  // Signals that a temporary buffer is no longer needed.
+  void DeallocateTemp(uint8_t* buf) override;
+
+  // Returns true if all temporary buffers are already deallocated.
+  bool IsAllTempDeallocated() override;
+
+  // Signals that all temporary allocations can be reclaimed. TFLM calls this
+  // API when it knows that all temporary buffers that it requested has been
+  // deallocated.
+  TfLiteStatus ResetTempAllocations() override;
+
+  // Returns a buffer that is resizable viable ResizeBuffer().
+  uint8_t* AllocateResizableBuffer(size_t size, size_t alignment) override;
+
+  // Resizes a buffer that is previously returned by the
+  // AllocateResizableBuffer.
+  TfLiteStatus ResizeBuffer(uint8_t* resizable_buf, size_t size,
+                            size_t alignment) override;
+
+  // Frees up the memory occupied by the resizable buffer.
+  TfLiteStatus DeallocateResizableBuffer(uint8_t* resizable_buf) override;
+
+  // Returns a pointer pointing to the start of the overlay memory, which is
+  // used for activation tensors and scratch buffers by kernels at Invoke stage.
+  uint8_t* GetOverlayMemoryAddress() const override;
+
+  // Reserves the size of the overlay memory. This overlay is reserved for the
+  // kernels at Invoke stage. This is referred to as the overlay because before
+  // Invoket state, the same memory can be used for temp buffers. The layout of
+  // the memory is planned by the memory planner separately at Invoke stage.
+  TfLiteStatus ReserveNonPersistentOverlayMemory(size_t size,
+                                                 size_t alignment) override;
+
+  // Returns the size of non-persistent buffer in use.
+  size_t GetNonPersistentUsedBytes() const override;
+
+  // Returns the number of bytes available with a given alignment. This number
+  // takes in account any temporary allocations.
+  size_t GetAvailableMemory(size_t alignment) const override;
+
+  TF_LITE_REMOVE_VIRTUAL_DELETE
+
+ private:
+  // The memory arena that this allocator manages.
+  uint8_t* const buffer_head_;
+  uint8_t* const buffer_tail_;
+
+  // The whole region is split into two parts:
+  // buffer_head_ to head_temp_ - 1 belongs to the only resizable buffer.
+  // head_temp_ to buffer_tail_ can be used for (non-resizable) temp buffers.
+  uint8_t* head_temp_;
+
+  // next_temp_ points to the next available temp buffer allocation address and
+  // its range is between head_temp_ and buffer_tail_
+  uint8_t* next_temp_;
+
+  // XOR Check sum for outstanding temp buffers.
+  // If all temp buffers are deallocated OR no temp buffers are allocated,
+  // temp_buffer_ptr_check_sum_ == nullptr.
+  intptr_t temp_buffer_ptr_check_sum_ = 0;
+  // Count of outstanding temp buffers.
+  int temp_buffer_count_ = 0;
+  bool resizable_buffer_allocated_ = false;
+};
+
+}  // namespace tflite
+
+#endif  // TENSORFLOW_LITE_MICRO_ARENA_ALLOCATOR_NON_PERSISTENT_ARENA_BUFFER_ALLOCATOR_H_

code/components/tflite-lib/tensorflow/lite/micro/arena_allocator/persistent_arena_buffer_allocator.cc

@@ -0,0 +1,52 @@
+/* Copyright 2022 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+#include "tensorflow/lite/micro/arena_allocator/persistent_arena_buffer_allocator.h"
+
+#include "tensorflow/lite/micro/memory_helpers.h"
+#include "tensorflow/lite/micro/micro_error_reporter.h"
+
+namespace tflite {
+
+PersistentArenaBufferAllocator::PersistentArenaBufferAllocator(
+    uint8_t* buffer, size_t buffer_size)
+    : buffer_head_(buffer),
+      buffer_tail_(buffer + buffer_size),
+      tail_temp_(buffer_tail_) {}
+
+PersistentArenaBufferAllocator::~PersistentArenaBufferAllocator() {}
+
+uint8_t* PersistentArenaBufferAllocator::AllocatePersistentBuffer(
+    size_t size, size_t alignment) {
+  uint8_t* const aligned_result =
+      AlignPointerDown(tail_temp_ - size, alignment);
+  if (aligned_result < buffer_head_) {
+#ifndef TF_LITE_STRIP_ERROR_STRINGS
+    const size_t missing_memory = buffer_head_ - aligned_result;
+    MicroPrintf(
+        "Failed to allocate tail memory. Requested: %u, "
+        "available %u, missing: %u",
+        size, size - missing_memory, missing_memory);
+#endif
+    return nullptr;
+  }
+  tail_temp_ = aligned_result;
+  return aligned_result;
+}
+
+size_t PersistentArenaBufferAllocator::GetPersistentUsedBytes() const {
+  return buffer_tail_ - tail_temp_;
+}
+
+}  // namespace tflite

code/components/tflite-lib/tensorflow/lite/micro/arena_allocator/persistent_arena_buffer_allocator.h

@@ -0,0 +1,59 @@
+/* Copyright 2022 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+#ifndef TENSORFLOW_LITE_MICRO_ARENA_ALLOCATOR_PERSISTENT_ARENA_BUFFER_ALLOCATOR_H_
+#define TENSORFLOW_LITE_MICRO_ARENA_ALLOCATOR_PERSISTENT_ARENA_BUFFER_ALLOCATOR_H_
+
+#include <cstddef>
+#include <cstdint>
+
+#include "tensorflow/lite/c/common.h"
+#include "tensorflow/lite/core/api/error_reporter.h"
+#include "tensorflow/lite/micro/arena_allocator/ibuffer_allocator.h"
+#include "tensorflow/lite/micro/compatibility.h"
+
+namespace tflite {
+
+// PersistentArenaBufferAllocator is an implementatation of
+// IPersistentBufferAllocator interface on an arena that is dedicated for
+// persistent buffers.
+class PersistentArenaBufferAllocator : public IPersistentBufferAllocator {
+ public:
+  PersistentArenaBufferAllocator(uint8_t* buffer, size_t buffer_size);
+  virtual ~PersistentArenaBufferAllocator();
+
+  // Allocates persistent memory. The persistent buffer is never freed.
+  // Returns nullptr if errors occured.
+  uint8_t* AllocatePersistentBuffer(size_t size, size_t alignment) override;
+
+  // Returns the size of all persistent allocations in bytes.
+  size_t GetPersistentUsedBytes() const override;
+
+  TF_LITE_REMOVE_VIRTUAL_DELETE
+ private:
+  // The memory arena that this allocator manages.
+  uint8_t* const buffer_head_;
+  uint8_t* const buffer_tail_;
+
+  // The whole region is split into two parts:
+  // tail_temp_ to buffer_tail_ contains allocated buffers;
+  // buffer_head_ to tail_temp_ - 1 belongs to still available spaces.
+  // So in essence, the allocated region grows from the bottom and emulates
+  // SingleArenaBufferAllocator's persistent part.
+  uint8_t* tail_temp_;
+};
+
+}  // namespace tflite
+
+#endif  // TENSORFLOW_LITE_MICRO_ARENA_ALLOCATOR_PERSISTENT_ARENA_BUFFER_ALLOCATOR_H_

code/components/tflite-lib/tensorflow/lite/micro/arena_allocator/recording_single_arena_buffer_allocator.cc

@@ -13,7 +13,7 @@ See the License for the specific language governing permissions and
 limitations under the License.
 ==============================================================================*/
 
-#include "tensorflow/lite/micro/recording_simple_memory_allocator.h"
+#include "tensorflow/lite/micro/arena_allocator/recording_single_arena_buffer_allocator.h"
 
 #include <new>
 
@@ -21,47 +21,49 @@ limitations under the License.
 
 namespace tflite {
 
-RecordingSimpleMemoryAllocator::RecordingSimpleMemoryAllocator(
+RecordingSingleArenaBufferAllocator::RecordingSingleArenaBufferAllocator(
     ErrorReporter* error_reporter, uint8_t* buffer_head, size_t buffer_size)
-    : SimpleMemoryAllocator(error_reporter, buffer_head, buffer_size),
+    : SingleArenaBufferAllocator(error_reporter, buffer_head, buffer_size),
       requested_head_bytes_(0),
       requested_tail_bytes_(0),
       used_bytes_(0),
       alloc_count_(0) {}
 
-RecordingSimpleMemoryAllocator::~RecordingSimpleMemoryAllocator() {}
+RecordingSingleArenaBufferAllocator::~RecordingSingleArenaBufferAllocator() {}
 
-RecordingSimpleMemoryAllocator* RecordingSimpleMemoryAllocator::Create(
-    ErrorReporter* error_reporter, uint8_t* buffer_head, size_t buffer_size) {
+RecordingSingleArenaBufferAllocator*
+RecordingSingleArenaBufferAllocator::Create(ErrorReporter* error_reporter,
+                                            uint8_t* buffer_head,
+                                            size_t buffer_size) {
   TFLITE_DCHECK(error_reporter != nullptr);
   TFLITE_DCHECK(buffer_head != nullptr);
-  RecordingSimpleMemoryAllocator tmp =
-      RecordingSimpleMemoryAllocator(error_reporter, buffer_head, buffer_size);
+  RecordingSingleArenaBufferAllocator tmp = RecordingSingleArenaBufferAllocator(
+      error_reporter, buffer_head, buffer_size);
 
-  uint8_t* allocator_buffer =
-      tmp.AllocatePersistentBuffer(sizeof(RecordingSimpleMemoryAllocator),
-                                   alignof(RecordingSimpleMemoryAllocator));
+  uint8_t* allocator_buffer = tmp.AllocatePersistentBuffer(
+      sizeof(RecordingSingleArenaBufferAllocator),
+      alignof(RecordingSingleArenaBufferAllocator));
   // Use the default copy constructor to populate internal states.
-  return new (allocator_buffer) RecordingSimpleMemoryAllocator(tmp);
+  return new (allocator_buffer) RecordingSingleArenaBufferAllocator(tmp);
 }
 
-size_t RecordingSimpleMemoryAllocator::GetRequestedBytes() const {
+size_t RecordingSingleArenaBufferAllocator::GetRequestedBytes() const {
   return requested_head_bytes_ + requested_tail_bytes_;
 }
 
-size_t RecordingSimpleMemoryAllocator::GetUsedBytes() const {
+size_t RecordingSingleArenaBufferAllocator::GetUsedBytes() const {
   return used_bytes_;
 }
 
-size_t RecordingSimpleMemoryAllocator::GetAllocatedCount() const {
+size_t RecordingSingleArenaBufferAllocator::GetAllocatedCount() const {
   return alloc_count_;
 }
 
-TfLiteStatus RecordingSimpleMemoryAllocator::ResizeBuffer(
+TfLiteStatus RecordingSingleArenaBufferAllocator::ResizeBuffer(
     uint8_t* resizable_buf, size_t size, size_t alignment) {
   const uint8_t* previous_head = head();
   TfLiteStatus status =
-      SimpleMemoryAllocator::ResizeBuffer(resizable_buf, size, alignment);
+      SingleArenaBufferAllocator::ResizeBuffer(resizable_buf, size, alignment);
   if (status == kTfLiteOk) {
     used_bytes_ += head() - previous_head;
     requested_head_bytes_ = size;
@@ -69,11 +71,11 @@ TfLiteStatus RecordingSimpleMemoryAllocator::ResizeBuffer(
   return status;
 }
 
-uint8_t* RecordingSimpleMemoryAllocator::AllocatePersistentBuffer(
+uint8_t* RecordingSingleArenaBufferAllocator::AllocatePersistentBuffer(
     size_t size, size_t alignment) {
   const uint8_t* previous_tail = tail();
   uint8_t* result =
-      SimpleMemoryAllocator::AllocatePersistentBuffer(size, alignment);
+      SingleArenaBufferAllocator::AllocatePersistentBuffer(size, alignment);
   if (result != nullptr) {
     used_bytes_ += previous_tail - tail();
     requested_tail_bytes_ += size;

code/components/tflite-lib/tensorflow/lite/micro/arena_allocator/recording_single_arena_buffer_allocator.h

@@ -13,28 +13,27 @@ See the License for the specific language governing permissions and
 limitations under the License.
 ==============================================================================*/
 
-#ifndef TENSORFLOW_LITE_MICRO_RECORDING_SIMPLE_MEMORY_ALLOCATOR_H_
-#define TENSORFLOW_LITE_MICRO_RECORDING_SIMPLE_MEMORY_ALLOCATOR_H_
+#ifndef TENSORFLOW_LITE_MICRO_ARENA_ALLOCATOR_RECORDING_SINGLE_ARENA_BUFFER_ALLOCATOR_H_
+#define TENSORFLOW_LITE_MICRO_ARENA_ALLOCATOR_RECORDING_SINGLE_ARENA_BUFFER_ALLOCATOR_H_
 
+#include "tensorflow/lite/micro/arena_allocator/single_arena_buffer_allocator.h"
 #include "tensorflow/lite/micro/compatibility.h"
-#include "tensorflow/lite/micro/simple_memory_allocator.h"
 
 namespace tflite {
 
-// Utility class used to log allocations of a SimpleMemoryAllocator. Should only
-// be used in debug/evaluation settings or unit tests to evaluate allocation
-// usage.
-class RecordingSimpleMemoryAllocator : public SimpleMemoryAllocator {
+// Utility class used to log allocations of a SingleArenaBufferAllocator. Should
+// only be used in debug/evaluation settings or unit tests to evaluate
+// allocation usage.
+class RecordingSingleArenaBufferAllocator : public SingleArenaBufferAllocator {
  public:
-  RecordingSimpleMemoryAllocator(ErrorReporter* error_reporter,
-                                 uint8_t* buffer_head, size_t buffer_size);
+  RecordingSingleArenaBufferAllocator(ErrorReporter* error_reporter,
+                                      uint8_t* buffer_head, size_t buffer_size);
   // TODO(b/157615197): Cleanup constructors/destructor and use factory
   // functions.
-  ~RecordingSimpleMemoryAllocator() override;
+  ~RecordingSingleArenaBufferAllocator() override;
 
-  static RecordingSimpleMemoryAllocator* Create(ErrorReporter* error_reporter,
-                                                uint8_t* buffer_head,
-                                                size_t buffer_size);
+  static RecordingSingleArenaBufferAllocator* Create(
+      ErrorReporter* error_reporter, uint8_t* buffer_head, size_t buffer_size);
 
   // Returns the number of bytes requested from the head or tail.
   size_t GetRequestedBytes() const;
@@ -62,4 +61,4 @@ class RecordingSimpleMemoryAllocator : public SimpleMemoryAllocator {
 
 }  // namespace tflite
 
-#endif  // TENSORFLOW_LITE_MICRO_RECORDING_SIMPLE_MEMORY_ALLOCATOR_H_
+#endif  // TENSORFLOW_LITE_MICRO_ARENA_ALLOCATOR_RECORDING_SINGLE_ARENA_BUFFER_ALLOCATOR_H_

code/components/tflite-lib/tensorflow/lite/micro/arena_allocator/single_arena_buffer_allocator.cc

@@ -13,7 +13,7 @@ See the License for the specific language governing permissions and
 limitations under the License.
 ==============================================================================*/
 
-#include "tensorflow/lite/micro/simple_memory_allocator.h"
+#include "tensorflow/lite/micro/arena_allocator/single_arena_buffer_allocator.h"
 
 #include <cstddef>
 #include <cstdint>
@@ -29,42 +29,45 @@ limitations under the License.
 
 namespace tflite {
 
-SimpleMemoryAllocator::SimpleMemoryAllocator(ErrorReporter* error_reporter,
-                                             uint8_t* buffer_head,
-                                             uint8_t* buffer_tail)
-    : error_reporter_(error_reporter),
+SingleArenaBufferAllocator::SingleArenaBufferAllocator(
+    ErrorReporter* error_reporter, uint8_t* buffer_head, uint8_t* buffer_tail)
+    :
+#if !defined(TF_LITE_STRIP_ERROR_STRINGS)
+      error_reporter_(error_reporter),
+#endif
       buffer_head_(buffer_head),
       buffer_tail_(buffer_tail),
       head_(buffer_head),
       tail_(buffer_tail),
-      temp_(buffer_head_) {}
+      temp_(buffer_head_) {
+}
 
-SimpleMemoryAllocator::SimpleMemoryAllocator(ErrorReporter* error_reporter,
-                                             uint8_t* buffer,
-                                             size_t buffer_size)
-    : SimpleMemoryAllocator(error_reporter, buffer, buffer + buffer_size) {}
+SingleArenaBufferAllocator::SingleArenaBufferAllocator(
+    ErrorReporter* error_reporter, uint8_t* buffer, size_t buffer_size)
+    : SingleArenaBufferAllocator(error_reporter, buffer, buffer + buffer_size) {
+}
 
 /* static */
-SimpleMemoryAllocator* SimpleMemoryAllocator::Create(
+SingleArenaBufferAllocator* SingleArenaBufferAllocator::Create(
     ErrorReporter* error_reporter, uint8_t* buffer_head, size_t buffer_size) {
   TFLITE_DCHECK(error_reporter != nullptr);
   TFLITE_DCHECK(buffer_head != nullptr);
-  SimpleMemoryAllocator tmp =
-      SimpleMemoryAllocator(error_reporter, buffer_head, buffer_size);
+  SingleArenaBufferAllocator tmp =
+      SingleArenaBufferAllocator(error_reporter, buffer_head, buffer_size);
 
-  // Allocate enough bytes from the buffer to create a SimpleMemoryAllocator.
-  // The new instance will use the current adjusted tail buffer from the tmp
-  // allocator instance.
+  // Allocate enough bytes from the buffer to create a
+  // SingleArenaBufferAllocator. The new instance will use the current adjusted
+  // tail buffer from the tmp allocator instance.
   uint8_t* allocator_buffer = tmp.AllocatePersistentBuffer(
-      sizeof(SimpleMemoryAllocator), alignof(SimpleMemoryAllocator));
+      sizeof(SingleArenaBufferAllocator), alignof(SingleArenaBufferAllocator));
   // Use the default copy constructor to populate internal states.
-  return new (allocator_buffer) SimpleMemoryAllocator(tmp);
+  return new (allocator_buffer) SingleArenaBufferAllocator(tmp);
 }
 
-SimpleMemoryAllocator::~SimpleMemoryAllocator() {}
+SingleArenaBufferAllocator::~SingleArenaBufferAllocator() {}
 
-uint8_t* SimpleMemoryAllocator::AllocateResizableBuffer(size_t size,
-                                                        size_t alignment) {
+uint8_t* SingleArenaBufferAllocator::AllocateResizableBuffer(size_t size,
+                                                             size_t alignment) {
   // Only supports one resizable buffer, which starts at the buffer head.
   uint8_t* expect_resizable_buf = AlignPointerUp(buffer_head_, alignment);
   if (ResizeBuffer(expect_resizable_buf, size, alignment) == kTfLiteOk) {
@@ -73,20 +76,20 @@ uint8_t* SimpleMemoryAllocator::AllocateResizableBuffer(size_t size,
   return nullptr;
 }
 
-TfLiteStatus SimpleMemoryAllocator::DeallocateResizableBuffer(
+TfLiteStatus SingleArenaBufferAllocator::DeallocateResizableBuffer(
     uint8_t* resizable_buf) {
   return ResizeBuffer(resizable_buf, 0, 1);
 }
 
-TfLiteStatus SimpleMemoryAllocator::ReserveNonPersistentOverlayMemory(
+TfLiteStatus SingleArenaBufferAllocator::ReserveNonPersistentOverlayMemory(
     size_t size, size_t alignment) {
   uint8_t* expect_resizable_buf = AlignPointerUp(buffer_head_, alignment);
   return ResizeBuffer(expect_resizable_buf, size, alignment);
 }
 
-TfLiteStatus SimpleMemoryAllocator::ResizeBuffer(uint8_t* resizable_buf,
-                                                 size_t size,
-                                                 size_t alignment) {
+TfLiteStatus SingleArenaBufferAllocator::ResizeBuffer(uint8_t* resizable_buf,
+                                                      size_t size,
+                                                      size_t alignment) {
   // Only supports one resizable buffer, which starts at the buffer head.
   uint8_t* expect_resizable_buf = AlignPointerUp(buffer_head_, alignment);
   if (head_ != temp_ || resizable_buf != expect_resizable_buf) {
@@ -112,8 +115,8 @@ TfLiteStatus SimpleMemoryAllocator::ResizeBuffer(uint8_t* resizable_buf,
   return kTfLiteOk;
 }
 
-uint8_t* SimpleMemoryAllocator::AllocatePersistentBuffer(size_t size,
-                                                         size_t alignment) {
+uint8_t* SingleArenaBufferAllocator::AllocatePersistentBuffer(
+    size_t size, size_t alignment) {
   uint8_t* const aligned_result = AlignPointerDown(tail_ - size, alignment);
   if (aligned_result < head_) {
 #ifndef TF_LITE_STRIP_ERROR_STRINGS
@@ -129,7 +132,8 @@ uint8_t* SimpleMemoryAllocator::AllocatePersistentBuffer(size_t size,
   return aligned_result;
 }
 
-uint8_t* SimpleMemoryAllocator::AllocateTemp(size_t size, size_t alignment) {
+uint8_t* SingleArenaBufferAllocator::AllocateTemp(size_t size,
+                                                  size_t alignment) {
   uint8_t* const aligned_result = AlignPointerUp(temp_, alignment);
   const size_t available_memory = tail_ - aligned_result;
   if (available_memory < size) {
@@ -145,12 +149,12 @@ uint8_t* SimpleMemoryAllocator::AllocateTemp(size_t size, size_t alignment) {
   return aligned_result;
 }
 
-void SimpleMemoryAllocator::DeallocateTemp(uint8_t* temp_buf) {
+void SingleArenaBufferAllocator::DeallocateTemp(uint8_t* temp_buf) {
   temp_buffer_ptr_check_sum_ ^= (reinterpret_cast<intptr_t>(temp_buf));
   temp_buffer_count_--;
 }
 
-bool SimpleMemoryAllocator::IsAllTempDeallocated() {
+bool SingleArenaBufferAllocator::IsAllTempDeallocated() {
   if (temp_buffer_count_ != 0 || temp_buffer_ptr_check_sum_ != 0) {
     MicroPrintf(
         "Number of allocated temp buffers: %d. Checksum passing status: %d",
@@ -160,7 +164,7 @@ bool SimpleMemoryAllocator::IsAllTempDeallocated() {
   return true;
 }
 
-TfLiteStatus SimpleMemoryAllocator::ResetTempAllocations() {
+TfLiteStatus SingleArenaBufferAllocator::ResetTempAllocations() {
   // TODO(b/209453859): enable error check based on IsAllTempDeallocated after
   // all AllocateTemp have been paird with DeallocateTemp
   if (!IsAllTempDeallocated()) {
@@ -172,34 +176,34 @@ TfLiteStatus SimpleMemoryAllocator::ResetTempAllocations() {
   return kTfLiteOk;
 }
 
-uint8_t* SimpleMemoryAllocator::GetOverlayMemoryAddress() const {
+uint8_t* SingleArenaBufferAllocator::GetOverlayMemoryAddress() const {
   return buffer_head_;
 }
 
-size_t SimpleMemoryAllocator::GetNonPersistentUsedBytes() const {
+size_t SingleArenaBufferAllocator::GetNonPersistentUsedBytes() const {
   return std::max(head_ - buffer_head_, temp_ - buffer_head_);
 }
 
-size_t SimpleMemoryAllocator::GetPersistentUsedBytes() const {
+size_t SingleArenaBufferAllocator::GetPersistentUsedBytes() const {
   return buffer_tail_ - tail_;
 }
 
-size_t SimpleMemoryAllocator::GetAvailableMemory(size_t alignment) const {
+size_t SingleArenaBufferAllocator::GetAvailableMemory(size_t alignment) const {
   uint8_t* const aligned_temp = AlignPointerUp(temp_, alignment);
   uint8_t* const aligned_tail = AlignPointerDown(tail_, alignment);
   return aligned_tail - aligned_temp;
 }
 
-size_t SimpleMemoryAllocator::GetUsedBytes() const {
+size_t SingleArenaBufferAllocator::GetUsedBytes() const {
   return GetPersistentUsedBytes() + GetNonPersistentUsedBytes();
 }
 
-size_t SimpleMemoryAllocator::GetBufferSize() const {
+size_t SingleArenaBufferAllocator::GetBufferSize() const {
   return buffer_tail_ - buffer_head_;
 }
 
-uint8_t* SimpleMemoryAllocator::head() const { return head_; }
+uint8_t* SingleArenaBufferAllocator::head() const { return head_; }
 
-uint8_t* SimpleMemoryAllocator::tail() const { return tail_; }
+uint8_t* SingleArenaBufferAllocator::tail() const { return tail_; }
 
 }  // namespace tflite

code/components/tflite-lib/tensorflow/lite/micro/arena_allocator/single_arena_buffer_allocator.h

@@ -13,37 +13,37 @@ See the License for the specific language governing permissions and
 limitations under the License.
 ==============================================================================*/
 
-#ifndef TENSORFLOW_LITE_MICRO_SIMPLE_MEMORY_ALLOCATOR_H_
-#define TENSORFLOW_LITE_MICRO_SIMPLE_MEMORY_ALLOCATOR_H_
+#ifndef TENSORFLOW_LITE_MICRO_ARENA_ALLOCATOR_SINGLE_ARENA_BUFFER_ALLOCATOR_H_
+#define TENSORFLOW_LITE_MICRO_ARENA_ALLOCATOR_SINGLE_ARENA_BUFFER_ALLOCATOR_H_
 
 #include <cstddef>
 #include <cstdint>
 
 #include "tensorflow/lite/c/common.h"
 #include "tensorflow/lite/core/api/error_reporter.h"
+#include "tensorflow/lite/micro/arena_allocator/ibuffer_allocator.h"
 #include "tensorflow/lite/micro/compatibility.h"
-#include "tensorflow/lite/micro/ibuffer_allocator.h"
 
 namespace tflite {
 
 // TODO(petewarden): This allocator never frees up or reuses  any memory, even
 // though we have enough information about lifetimes of the tensors to do so.
 // This makes it pretty wasteful, so we should use a more intelligent method.
-class SimpleMemoryAllocator : public INonPersistentBufferAllocator,
-                              public IPersistentBufferAllocator {
+class SingleArenaBufferAllocator : public INonPersistentBufferAllocator,
+                                   public IPersistentBufferAllocator {
  public:
   // TODO(b/157615197): Cleanup constructors/destructor and use factory
   // functions.
-  SimpleMemoryAllocator(ErrorReporter* error_reporter, uint8_t* buffer_head,
-                        uint8_t* buffer_tail);
-  SimpleMemoryAllocator(ErrorReporter* error_reporter, uint8_t* buffer,
-                        size_t buffer_size);
-  virtual ~SimpleMemoryAllocator();
+  SingleArenaBufferAllocator(ErrorReporter* error_reporter,
+                             uint8_t* buffer_head, uint8_t* buffer_tail);
+  SingleArenaBufferAllocator(ErrorReporter* error_reporter, uint8_t* buffer,
+                             size_t buffer_size);
+  virtual ~SingleArenaBufferAllocator();
 
-  // Creates a new SimpleMemoryAllocator from a given buffer head and size.
-  static SimpleMemoryAllocator* Create(ErrorReporter* error_reporter,
-                                       uint8_t* buffer_head,
-                                       size_t buffer_size);
+  // Creates a new SingleArenaBufferAllocator from a given buffer head and size.
+  static SingleArenaBufferAllocator* Create(ErrorReporter* error_reporter,
+                                            uint8_t* buffer_head,
+                                            size_t buffer_size);
 
   // Resizes a buffer that is previously returned by the
   // AllocateResizableBuffer. In current implementation, it Adjusts the head
@@ -126,7 +126,9 @@ class SimpleMemoryAllocator : public INonPersistentBufferAllocator,
  private:
   size_t GetBufferSize() const;
 
+#if !defined(TF_LITE_STRIP_ERROR_STRINGS)
   ErrorReporter* error_reporter_;
+#endif
   uint8_t* buffer_head_;
   uint8_t* buffer_tail_;
   uint8_t* head_;
@@ -147,4 +149,4 @@ class SimpleMemoryAllocator : public INonPersistentBufferAllocator,
 
 }  // namespace tflite
 
-#endif  // TENSORFLOW_LITE_MICRO_SIMPLE_MEMORY_ALLOCATOR_H_
+#endif  // TENSORFLOW_LITE_MICRO_ARENA_ALLOCATOR_SINGLE_ARENA_BUFFER_ALLOCATOR_H_

code/components/tflite-lib/tensorflow/lite/micro/fake_micro_context.cc

@@ -16,10 +16,10 @@ limitations under the License.
 #include "tensorflow/lite/micro/fake_micro_context.h"
 
 #include "tensorflow/lite/kernels/internal/compatibility.h"
+#include "tensorflow/lite/micro/arena_allocator/single_arena_buffer_allocator.h"
 #include "tensorflow/lite/micro/micro_allocator.h"
 #include "tensorflow/lite/micro/micro_arena_constants.h"
 #include "tensorflow/lite/micro/micro_error_reporter.h"
-#include "tensorflow/lite/micro/simple_memory_allocator.h"
 
 namespace tflite {
 namespace {
@@ -30,7 +30,7 @@ static uint8_t dummy_tensor_arena[KDummyTensorArenaSize];
 }  // namespace
 
 FakeMicroContext::FakeMicroContext(TfLiteTensor* tensors,
-                                   SimpleMemoryAllocator* allocator,
+                                   SingleArenaBufferAllocator* allocator,
                                    MicroGraph* micro_graph)
     : MicroContext(
           MicroAllocator::Create(dummy_tensor_arena, KDummyTensorArenaSize,
@@ -67,10 +67,10 @@ TfLiteEvalTensor* FakeMicroContext::GetEvalTensor(int tensor_index) {
 }
 
 void* FakeMicroContext::AllocatePersistentBuffer(size_t bytes) {
-  // FakeMicroContext use SimpleMemoryAllocator, which does not automatically
-  // apply the buffer alignment like MicroAllocator.
-  // The buffer alignment is potentially wasteful but allows the
-  // fake_micro_context to work correctly with optimized kernels.
+  // FakeMicroContext use SingleArenaBufferAllocator, which does not
+  // automatically apply the buffer alignment like MicroAllocator. The buffer
+  // alignment is potentially wasteful but allows the fake_micro_context to work
+  // correctly with optimized kernels.
   return allocator_->AllocatePersistentBuffer(bytes,
                                               MicroArenaBufferAlignment());
 }

code/components/tflite-lib/tensorflow/lite/micro/fake_micro_context.h

@@ -23,7 +23,7 @@ namespace tflite {
 // A fake of MicroContext for kernel util tests.
 class FakeMicroContext : public MicroContext {
  public:
-  FakeMicroContext(TfLiteTensor* tensors, SimpleMemoryAllocator* allocator,
+  FakeMicroContext(TfLiteTensor* tensors, SingleArenaBufferAllocator* allocator,
                    MicroGraph* micro_graph);
 
   void* AllocatePersistentBuffer(size_t bytes) override;
@@ -46,7 +46,7 @@ class FakeMicroContext : public MicroContext {
   TfLiteTensor* tensors_;
   int allocated_tensor_count_ = 0;
 
-  SimpleMemoryAllocator* allocator_;
+  SingleArenaBufferAllocator* allocator_;
 
   TF_LITE_REMOVE_VIRTUAL_DELETE
 };

code/components/tflite-lib/tensorflow/lite/micro/kernels/activations.cc

@@ -24,6 +24,7 @@ limitations under the License.
 #include "tensorflow/lite/kernels/kernel_util.h"
 #include "tensorflow/lite/kernels/op_macros.h"
 #include "tensorflow/lite/micro/kernels/kernel_util.h"
+#include "tensorflow/lite/micro/micro_error_reporter.h"
 #include "tensorflow/lite/micro/micro_utils.h"
 
 namespace tflite {
@@ -60,8 +61,8 @@ TfLiteStatus ReluEval(TfLiteContext* context, TfLiteNode* node) {
       return kTfLiteOk;
     }
     default: {
-      TF_LITE_KERNEL_LOG(context, "Only float32 is supported currently, got %s",
-                         TfLiteTypeGetName(input->type));
+      MicroPrintf("Only float32 is supported currently, got %s",
+                  TfLiteTypeGetName(input->type));
       return kTfLiteError;
     }
   }
@@ -99,8 +100,8 @@ TfLiteStatus Relu6Eval(TfLiteContext* context, TfLiteNode* node) {
       return kTfLiteOk;
     }
     default: {
-      TF_LITE_KERNEL_LOG(context, "Only float32 is supported currently, got %s",
-                         TfLiteTypeGetName(input->type));
+      MicroPrintf("Only float32 is supported currently, got %s",
+                  TfLiteTypeGetName(input->type));
       return kTfLiteError;
     }
   }
@@ -109,25 +110,11 @@ TfLiteStatus Relu6Eval(TfLiteContext* context, TfLiteNode* node) {
 }  // namespace
 
 TfLiteRegistration Register_RELU() {
-  return {/*init=*/ReluInit,
-          /*free=*/nullptr,
-          /*prepare=*/ReluPrepare,
-          /*invoke=*/ReluEval,
-          /*profiling_string=*/nullptr,
-          /*builtin_code=*/0,
-          /*custom_name=*/nullptr,
-          /*version=*/0};
+  return tflite::micro::RegisterOp(ReluInit, ReluPrepare, ReluEval);
 }
 
 TfLiteRegistration Register_RELU6() {
-  return {/*init=*/Relu6Init,
-          /*free=*/nullptr,
-          /*prepare=*/Relu6Prepare,
-          /*invoke=*/Relu6Eval,
-          /*profiling_string=*/nullptr,
-          /*builtin_code=*/0,
-          /*custom_name=*/nullptr,
-          /*version=*/0};
+  return tflite::micro::RegisterOp(Relu6Init, Relu6Prepare, Relu6Eval);
 }
 
 }  // namespace tflite

code/components/tflite-lib/tensorflow/lite/micro/kernels/add.cc

@@ -159,14 +159,7 @@ TfLiteStatus AddEval(TfLiteContext* context, TfLiteNode* node) {
 }
 
 TfLiteRegistration Register_ADD() {
-  return {/*init=*/AddInit,
-          /*free=*/nullptr,
-          /*prepare=*/AddPrepare,
-          /*invoke=*/AddEval,
-          /*profiling_string=*/nullptr,
-          /*builtin_code=*/0,
-          /*custom_name=*/nullptr,
-          /*version=*/0};
+  return tflite::micro::RegisterOp(AddInit, AddPrepare, AddEval);
 }
 
 }  // namespace tflite

code/components/tflite-lib/tensorflow/lite/micro/kernels/add_n.cc

@@ -208,14 +208,7 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
 }  // namespace
 
 TfLiteRegistration Register_ADD_N() {
-  return {/*init=*/nullptr,
-          /*free=*/nullptr,
-          /*prepare=*/Prepare,
-          /*invoke=*/Eval,
-          /*profiling_string=*/nullptr,
-          /*builtin_code=*/0,
-          /*custom_name=*/nullptr,
-          /*version=*/0};
+  return tflite::micro::RegisterOp(nullptr, Prepare, Eval);
 }
 
 }  // namespace tflite

code/components/tflite-lib/tensorflow/lite/micro/kernels/arg_min_max.cc

@@ -104,25 +104,11 @@ TfLiteStatus ArgMaxEval(TfLiteContext* context, TfLiteNode* node) {
 }  // namespace arg_min_max
 
 TfLiteRegistration Register_ARG_MAX() {
-  return {/*init=*/nullptr,
-          /*free=*/nullptr,
-          /*prepare=*/nullptr,
-          /*invoke=*/arg_min_max::ArgMaxEval,
-          /*profiling_string=*/nullptr,
-          /*builtin_code=*/0,
-          /*custom_name=*/nullptr,
-          /*version=*/0};
+  return tflite::micro::RegisterOp(nullptr, nullptr, arg_min_max::ArgMaxEval);
 }
 
 TfLiteRegistration Register_ARG_MIN() {
-  return {/*init=*/nullptr,
-          /*free=*/nullptr,
-          /*prepare=*/nullptr,
-          /*invoke=*/arg_min_max::ArgMinEval,
-          /*profiling_string=*/nullptr,
-          /*builtin_code=*/0,
-          /*custom_name=*/nullptr,
-          /*version=*/0};
+  return tflite::micro::RegisterOp(nullptr, nullptr, arg_min_max::ArgMinEval);
 }
 
 }  // namespace micro

code/components/tflite-lib/tensorflow/lite/micro/kernels/assign_variable.cc

@@ -95,14 +95,7 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
 }  // namespace.
 
 TfLiteRegistration Register_ASSIGN_VARIABLE() {
-  return {/*init=*/nullptr,
-          /*free=*/nullptr,
-          /*prepare=*/Prepare,
-          /*invoke=*/Eval,
-          /*profiling_string=*/nullptr,
-          /*builtin_code=*/0,
-          /*custom_name=*/nullptr,
-          /*version=*/0};
+  return tflite::micro::RegisterOp(nullptr, Prepare, Eval);
 }
 
 }  // namespace tflite

code/components/tflite-lib/tensorflow/lite/micro/kernels/batch_to_space_nd.cc

@@ -105,14 +105,7 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
 }  // namespace.
 
 TfLiteRegistration Register_BATCH_TO_SPACE_ND() {
-  return {/*init=*/nullptr,
-          /*free=*/nullptr,
-          /*prepare=*/Prepare,
-          /*invoke=*/Eval,
-          /*profiling_string=*/nullptr,
-          /*builtin_code=*/0,
-          /*custom_name=*/nullptr,
-          /*version=*/0};
+  return tflite::micro::RegisterOp(nullptr, Prepare, Eval);
 }
 
 }  // namespace tflite

code/components/tflite-lib/tensorflow/lite/micro/kernels/broadcast_args.cc

@@ -84,14 +84,8 @@ TfLiteStatus BroadcastArgsEval(TfLiteContext* context, TfLiteNode* node) {
 }  // namespace
 
 TfLiteRegistration Register_BROADCAST_ARGS() {
-  return {/*init=*/nullptr,
-          /*free=*/nullptr,
-          /*prepare=*/BroadcastArgsPrepare,
-          /*invoke=*/BroadcastArgsEval,
-          /*profiling_string=*/nullptr,
-          /*builtin_code=*/0,
-          /*custom_name=*/nullptr,
-          /*version=*/0};
+  return tflite::micro::RegisterOp(nullptr, BroadcastArgsPrepare,
+                                   BroadcastArgsEval);
 }
 
-}  // namespace tflite
+}  // namespace tflite

code/components/tflite-lib/tensorflow/lite/micro/kernels/broadcast_to.cc

@@ -116,14 +116,8 @@ TfLiteStatus BroadcastToEval(TfLiteContext* context, TfLiteNode* node) {
 }  // namespace
 
 TfLiteRegistration Register_BROADCAST_TO() {
-  return {/*init=*/nullptr,
-          /*free=*/nullptr,
-          /*prepare=*/BroadcastToPrepare,
-          /*invoke=*/BroadcastToEval,
-          /*profiling_string=*/nullptr,
-          /*builtin_code=*/0,
-          /*custom_name=*/nullptr,
-          /*version=*/0};
+  return tflite::micro::RegisterOp(nullptr, BroadcastToPrepare,
+                                   BroadcastToEval);
 }
 
-}  // namespace tflite
+}  // namespace tflite

code/components/tflite-lib/tensorflow/lite/micro/kernels/call_once.cc

@@ -82,14 +82,7 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
 }  // namespace.
 
 TfLiteRegistration Register_CALL_ONCE() {
-  return {/*init=*/Init,
-          /*free=*/nullptr,
-          /*prepare=*/Prepare,
-          /*invoke=*/Eval,
-          /*profiling_string=*/nullptr,
-          /*builtin_code=*/0,
-          /*custom_name=*/nullptr,
-          /*version=*/0};
+  return tflite::micro::RegisterOp(Init, Prepare, Eval);
 }
 
 }  // namespace tflite

code/components/tflite-lib/tensorflow/lite/micro/kernels/cast.cc

@@ -108,14 +108,7 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
 }  // namespace
 
 TfLiteRegistration Register_CAST() {
-  return {/*init=*/nullptr,
-          /*free=*/nullptr,
-          /*prepare=*/Prepare,
-          /*invoke=*/Eval,
-          /*profiling_string=*/nullptr,
-          /*builtin_code=*/0,
-          /*custom_name=*/nullptr,
-          /*version=*/0};
+  return tflite::micro::RegisterOp(nullptr, Prepare, Eval);
 }
 
 }  // namespace tflite

code/components/tflite-lib/tensorflow/lite/micro/kernels/ceil.cc

@@ -67,14 +67,7 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
 }  // namespace ceil
 
 TfLiteRegistration Register_CEIL() {
-  return {/*init=*/nullptr,
-          /*free=*/nullptr,
-          /*prepare=*/ceil::Prepare,
-          /*invoke=*/ceil::Eval,
-          /*profiling_string=*/nullptr,
-          /*builtin_code=*/0,
-          /*custom_name=*/nullptr,
-          /*version=*/0};
+  return tflite::micro::RegisterOp(nullptr, ceil::Prepare, ceil::Eval);
 }
 
 }  // namespace micro