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arm_nnfunctions.h
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arm_nnfunctions.h
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/*
* SPDX-FileCopyrightText: Copyright 2010-2023 Arm Limited and/or its affiliates <open-source-office@arm.com>
*
* SPDX-License-Identifier: Apache-2.0
*
* 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
*
* 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.
*/
/* ----------------------------------------------------------------------
* Project: CMSIS NN Library
* Title: arm_nnfunctions.h
* Description: Public header file for CMSIS NN Library
*
* $Date: 10 November 2023
* $Revision: V.12.4.0
*
* Target : Arm(R) M-Profile Architecture
* -------------------------------------------------------------------- */
/**
\mainpage CMSIS NN Software Library
*
* \tableofcontents
* \section Introduction
*
*
* This user manual describes the CMSIS NN software library,
* a collection of efficient neural network kernels developed to maximize the
* performance and minimize the memory footprint of neural networks on Arm Cortex-M processors.
*
* The library is divided into a number of functions each covering a specific category:
* - \ref NNConv
* - \ref Acti
* - \ref FC
* - \ref SVDF
* - \ref Pooling
* - \ref Softmax
* - \ref groupElementwise
* - \ref LSTM
*
* \section Processors Supported Processors
*
* CMSIS-NN targets Cortex-M processors with typically three different implementations for each function. Each
* targets a different group of processors.
* - Processors without Single Instruction Multiple Data(SIMD) capability (e.g, Cortex-M0)
* - Processors with DSP extension (e.g Cortex-M4)
* - Processors with Arm M-Profile Vector Extension(MVE) instructions (e.g Cortex-M55)
* The right implementation is picked through feature flags and the user does not have to explicit set it.
*
* \section Framework Quantization Specification
* The library follows the [int8](https://www.tensorflow.org/lite/performance/quantization_spec) and int16
* quantization specification of TensorFlow Lite for Microcontrollers.
* \section Overview Block Diagram
*
* \image html CMSIS-NN-OVERVIEW.PNG
*
* \section Examples
*
*
* An example image recognition application using TensorFlow Flow Lite for Microcontrollers as an inference engine
* and CMSIS-NN as the optimized library can be found in the Examples directory.
*
* \section Macros Pre-processor Macros
*
* \subsection Feature Feature flag based
* The macros below are defined in a build system based on feature flags for a chosen processor or architecture
* input to a compiler.
* These tie in to the classification in \ref Macros.
*
* For a CMSIS-NN file compiled as *armclang -mcpu=cortex-m4 --target=arm-arm-none-eabi -I<CMSIS Core Include>
* -Ofast -O file.c* , ARM_MATH_DSP is enabled as Cortex-M4 has the DSP extension as a feature.
*
* - `ARM_MATH_DSP` - Selects code for processors with DSP extension.
*
* - `ARM_MATH_MVEI` - Selects code for processors which supports MVE instructions.
*
* \subsection MiscFlags User Set
* - `ARM_MATH_AUTOVECTORIZE`
* Applicable when ARM_MATH_MVEI is active to let the compiler auto vectorize functions, if available, that uses
inline
* assembly. This has to be explicitly set at compile time.
*
* \section Inclusive Inclusive Language
* This product confirms to Arm’s inclusive language policy and, to the best of our knowledge,
* does not contain any non-inclusive language. If you find something that concerns you, email terms@arm.com.
*
* \section Copyright Copyright Notice
*
*
* SPDX-FileCopyrightText: Copyright 2010-2023 Arm Limited and/or its affiliates <open-source-office@arm.com>
*
*
*/
/**
* @defgroup Public Public
* A collection of functions to perform basic operations for neural network layers. Functions with a _s8 suffix support
* TensorFlow Lite framework.
*/
#ifndef _ARM_NNFUNCTIONS_H
#define _ARM_NNFUNCTIONS_H
#include "arm_nn_math_types.h"
#include "arm_nn_types.h"
#define USE_INTRINSIC
#ifdef __cplusplus
extern "C" {
#endif
/**
* @defgroup NNConv Convolution Functions
*
* Collection of convolution, depthwise convolution functions and their variants.
*
* The convolution is implemented in 2 steps: im2col and General Matrix Multiplication(GEMM)
*
* im2col is a process of converting each patch of image data into
* a column. After im2col, the convolution is computed as matrix-matrix
* multiplication.
*
* To reduce the memory footprint, the im2col is performed partially.
* Each iteration, only a few column (i.e., patches) are generated followed
* by GEMM.
*
*/
/**
* @brief s4 convolution layer wrapper function with the main purpose to call the optimal kernel available in
* cmsis-nn to perform the convolution.
*
* @param[in, out] ctx Function context that contains the additional buffer if required by the function.
* arm_convolve_wrapper_s4_get_buffer_size will return the buffer_size if required.
* The caller is expected to clear the buffer ,if applicable, for security reasons.
* @param[in] conv_params Convolution parameters (e.g. strides, dilations, pads,...).
* Range of conv_params->input_offset : [-127, 128]
* Range of conv_params->output_offset : [-128, 127]
* @param[in] quant_params Per-channel quantization info.
* It contains the multiplier and shift values to be applied to each output channel
* @param[in] input_dims Input (activation) tensor dimensions. Format: [N, H, W, C_IN]
* @param[in] input_data Input (activation) data pointer. Data type: int8
* @param[in] filter_dims Filter tensor dimensions. Format: [C_OUT, HK, WK, C_IN] where HK and WK are the
* spatial filter dimensions
* @param[in] filter_data Filter data pointer. Data type: int8 packed with 2x int4
* @param[in] bias_dims Bias tensor dimensions. Format: [C_OUT]
* @param[in] bias_data Bias data pointer. Data type: int32
* @param[in] output_dims Output tensor dimensions. Format: [N, H, W, C_OUT]
* @param[out] output_data Output data pointer. Data type: int8
*
* @return The function returns either
* <code>ARM_CMSIS_NN_ARG_ERROR</code> if argument constraints fail. or,
* <code>ARM_CMSIS_NN_SUCCESS</code> on successful completion.
*
*/
arm_cmsis_nn_status arm_convolve_wrapper_s4(const cmsis_nn_context *ctx,
const cmsis_nn_conv_params *conv_params,
const cmsis_nn_per_channel_quant_params *quant_params,
const cmsis_nn_dims *input_dims,
const int8_t *input_data,
const cmsis_nn_dims *filter_dims,
const int8_t *filter_data,
const cmsis_nn_dims *bias_dims,
const int32_t *bias_data,
const cmsis_nn_dims *output_dims,
int8_t *output_data);
/**
* @brief Get the required buffer size for arm_convolve_wrapper_s4
*
* @param[in] conv_params Convolution parameters (e.g. strides, dilations, pads,...).
* Range of conv_params->input_offset : [-127, 128]
* Range of conv_params->output_offset : [-128, 127]
* @param[in] input_dims Input (activation) dimensions. Format: [N, H, W, C_IN]
* @param[in] filter_dims Filter dimensions. Format: [C_OUT, HK, WK, C_IN] where HK and WK are the spatial
* filter dimensions
* @param[in] output_dims Output tensor dimensions. Format: [N, H, W, C_OUT]
*
* @return The function returns required buffer size(bytes)
*
*/
int32_t arm_convolve_wrapper_s4_get_buffer_size(const cmsis_nn_conv_params *conv_params,
const cmsis_nn_dims *input_dims,
const cmsis_nn_dims *filter_dims,
const cmsis_nn_dims *output_dims);
/**
* @brief Get the required buffer size for arm_convolve_wrapper_s4 for Arm(R) Helium Architecture case.
* Refer to arm_convolve_wrapper_s4_get_buffer_size() for function argument details.
*
* @note Intended for compilation on Host. If compiling for an Arm target, use
* arm_convolve_wrapper_s4_get_buffer_size(). Currently this operator does not have an
* mve implementation, so dsp will be used.
*
*/
int32_t arm_convolve_wrapper_s4_get_buffer_size_mve(const cmsis_nn_conv_params *conv_params,
const cmsis_nn_dims *input_dims,
const cmsis_nn_dims *filter_dims,
const cmsis_nn_dims *output_dims);
/**
* @brief Get the required buffer size for arm_convolve_wrapper_s4 for processors with DSP extension.
* Refer to arm_convolve_wrapper_s4_get_buffer_size() for function argument details.
*
* @note Intended for compilation on Host. If compiling for an Arm target, use
* arm_convolve_wrapper_s4_get_buffer_size().
*
*/
int32_t arm_convolve_wrapper_s4_get_buffer_size_dsp(const cmsis_nn_conv_params *conv_params,
const cmsis_nn_dims *input_dims,
const cmsis_nn_dims *filter_dims,
const cmsis_nn_dims *output_dims);
/**
* @brief s8 convolution layer wrapper function with the main purpose to call the optimal kernel available in
* cmsis-nn to perform the convolution.
*
* @param[in, out] ctx Function context that contains the additional buffer if required by the function.
* arm_convolve_wrapper_s8_get_buffer_size will return the buffer_size if required.
* The caller is expected to clear the buffer, if applicable, for security reasons.
* @param[in] conv_params Convolution parameters (e.g. strides, dilations, pads,...).
* Range of conv_params->input_offset : [-127, 128]
* Range of conv_params->output_offset : [-128, 127]
* @param[in] quant_params Per-channel quantization info.
* It contains the multiplier and shift values to be applied to each output channel
* @param[in] input_dims Input (activation) tensor dimensions. Format: [N, H, W, C_IN]
* @param[in] input_data Input (activation) data pointer. Data type: int8
* @param[in] filter_dims Filter tensor dimensions. Format: [C_OUT, HK, WK, C_IN] where HK and WK are the
* spatial filter dimensions
* @param[in] filter_data Filter data pointer. Data type: int8
* @param[in] bias_dims Bias tensor dimensions. Format: [C_OUT]
* @param[in] bias_data Bias data pointer. Data type: int32
* @param[in] output_dims Output tensor dimensions. Format: [N, H, W, C_OUT]
* @param[out] output_data Output data pointer. Data type: int8
*
* @return The function returns either
* <code>ARM_CMSIS_NN_ARG_ERROR</code> if argument constraints fail. or,
* <code>ARM_CMSIS_NN_SUCCESS</code> on successful completion.
*
*/
arm_cmsis_nn_status arm_convolve_wrapper_s8(const cmsis_nn_context *ctx,
const cmsis_nn_conv_params *conv_params,
const cmsis_nn_per_channel_quant_params *quant_params,
const cmsis_nn_dims *input_dims,
const int8_t *input_data,
const cmsis_nn_dims *filter_dims,
const int8_t *filter_data,
const cmsis_nn_dims *bias_dims,
const int32_t *bias_data,
const cmsis_nn_dims *output_dims,
int8_t *output_data);
/**
* @brief Get the required buffer size for arm_convolve_wrapper_s8
*
* @param[in] conv_params Convolution parameters (e.g. strides, dilations, pads,...).
* Range of conv_params->input_offset : [-127, 128]
* Range of conv_params->output_offset : [-128, 127]
* @param[in] input_dims Input (activation) dimensions. Format: [N, H, W, C_IN]
* @param[in] filter_dims Filter dimensions. Format: [C_OUT, HK, WK, C_IN] where HK and WK are the spatial
* filter dimensions
* @param[in] output_dims Output tensor dimensions. Format: [N, H, W, C_OUT]
*
* @return The function returns required buffer size(bytes)
*
*/
int32_t arm_convolve_wrapper_s8_get_buffer_size(const cmsis_nn_conv_params *conv_params,
const cmsis_nn_dims *input_dims,
const cmsis_nn_dims *filter_dims,
const cmsis_nn_dims *output_dims);
/**
* @brief Get the required buffer size for arm_convolve_wrapper_s8 for Arm(R) Helium Architecture case.
* Refer to arm_convolve_wrapper_s8_get_buffer_size() for function argument details.
*
* @note Intended for compilation on Host. If compiling for an Arm target, use
* arm_convolve_wrapper_s8_get_buffer_size().
*
*/
int32_t arm_convolve_wrapper_s8_get_buffer_size_mve(const cmsis_nn_conv_params *conv_params,
const cmsis_nn_dims *input_dims,
const cmsis_nn_dims *filter_dims,
const cmsis_nn_dims *output_dims);
/**
* @brief Get the required buffer size for arm_convolve_wrapper_s8 for processors with DSP extension.
* Refer to arm_convolve_wrapper_s8_get_buffer_size() for function argument details.
*
* @note Intended for compilation on Host. If compiling for an Arm target, use
* arm_convolve_wrapper_s8_get_buffer_size().
*
*/
int32_t arm_convolve_wrapper_s8_get_buffer_size_dsp(const cmsis_nn_conv_params *conv_params,
const cmsis_nn_dims *input_dims,
const cmsis_nn_dims *filter_dims,
const cmsis_nn_dims *output_dims);
/**
* @brief s16 convolution layer wrapper function with the main purpose to call the optimal kernel available in
* cmsis-nn to perform the convolution.
*
* @param[in, out] ctx Function context that contains the additional buffer if required by the function.
* arm_convolve_wrapper_s8_get_buffer_size will return the buffer_size if required
* The caller is expected to clear the buffer, if applicable, for security reasons.
* @param[in] conv_params Convolution parameters (e.g. strides, dilations, pads,...).
* conv_params->input_offset : Not used
* conv_params->output_offset : Not used
* @param[in] quant_params Per-channel quantization info.
* It contains the multiplier and shift values to be applied to each output channel
* @param[in] input_dims Input (activation) tensor dimensions. Format: [N, H, W, C_IN]
* @param[in] input_data Input (activation) data pointer. Data type: int16
* @param[in] filter_dims Filter tensor dimensions. Format: [C_OUT, HK, WK, C_IN] where HK and WK are the
* spatial filter dimensions
* @param[in] filter_data Filter data pointer. Data type: int8
* @param[in] bias_dims Bias tensor dimensions. Format: [C_OUT]
* @param[in] bias_data Bias data pointer. Data type: int64
* @param[in] output_dims Output tensor dimensions. Format: [N, H, W, C_OUT]
* @param[out] output_data Output data pointer. Data type: int16
*
* @return The function returns either
* <code>ARM_CMSIS_NN_ARG_ERROR</code> if argument constraints fail. or,
* <code>ARM_CMSIS_NN_SUCCESS</code> on successful completion.
*
*/
arm_cmsis_nn_status arm_convolve_wrapper_s16(const cmsis_nn_context *ctx,
const cmsis_nn_conv_params *conv_params,
const cmsis_nn_per_channel_quant_params *quant_params,
const cmsis_nn_dims *input_dims,
const int16_t *input_data,
const cmsis_nn_dims *filter_dims,
const int8_t *filter_data,
const cmsis_nn_dims *bias_dims,
const int64_t *bias_data,
const cmsis_nn_dims *output_dims,
int16_t *output_data);
/**
* @brief Get the required buffer size for arm_convolve_wrapper_s16.
*
* @param[in] conv_params Convolution parameters (e.g. strides, dilations, pads,...).
* conv_params->input_offset : Not used
* conv_params->output_offset : Not used
* @param[in] input_dims Input (activation) dimensions. Format: [N, H, W, C_IN]
* @param[in] filter_dims Filter dimensions. Format: [C_OUT, HK, WK, C_IN] where HK and WK are the spatial
* filter dimensions
* @param[in] output_dims Output tensor dimensions. Format: [N, H, W, C_OUT]
*
* @return The function returns required buffer size(bytes)
*
*/
int32_t arm_convolve_wrapper_s16_get_buffer_size(const cmsis_nn_conv_params *conv_params,
const cmsis_nn_dims *input_dims,
const cmsis_nn_dims *filter_dims,
const cmsis_nn_dims *output_dims);
/**
* @brief Get the required buffer size for arm_convolve_wrapper_s16 for for processors with DSP extension.
* Refer to arm_convolve_wrapper_s16_get_buffer_size() for function argument details.
*
* @note Intended for compilation on Host. If compiling for an Arm target, use
* arm_convolve_wrapper_s16_get_buffer_size().
*
*/
int32_t arm_convolve_wrapper_s16_get_buffer_size_dsp(const cmsis_nn_conv_params *conv_params,
const cmsis_nn_dims *input_dims,
const cmsis_nn_dims *filter_dims,
const cmsis_nn_dims *output_dims);
/**
* @brief Get the required buffer size for arm_convolve_wrapper_s16 for Arm(R) Helium Architecture case.
* Refer to arm_convolve_wrapper_s16_get_buffer_size() for function argument details.
*
* @note Intended for compilation on Host. If compiling for an Arm target, use
* arm_convolve_wrapper_s16_get_buffer_size().
*
*/
int32_t arm_convolve_wrapper_s16_get_buffer_size_mve(const cmsis_nn_conv_params *conv_params,
const cmsis_nn_dims *input_dims,
const cmsis_nn_dims *filter_dims,
const cmsis_nn_dims *output_dims);
/**
* @brief Basic s4 convolution function
* @param[in, out] ctx Function context that contains the additional buffer if required by the function.
* arm_convolve_s4_get_buffer_size will return the buffer_size if required.
* The caller is expected to clear the buffer ,if applicable, for security reasons.
* @param[in] conv_params Convolution parameters (e.g. strides, dilations, pads,...).
* Range of conv_params->input_offset : [-127, 128]
* Range of conv_params->output_offset : [-128, 127]
* @param[in] quant_params Per-channel quantization info.
* It contains the multiplier and shift values to be applied to each output channel
* @param[in] input_dims Input (activation) tensor dimensions. Format: [N, H, W, C_IN]
* @param[in] input_data Input (activation) data pointer. Data type: int8
* @param[in] filter_dims Filter tensor dimensions. Format: [C_OUT, HK, WK, C_IN] where HK and WK are the
* spatial filter dimensions
* @param[in] filter_data Packed Filter data pointer. Data type: int8 packed with 2x int4
* @param[in] bias_dims Bias tensor dimensions. Format: [C_OUT]
* @param[in] bias_data Optional bias data pointer. Data type: int32
* @param[in] output_dims Output tensor dimensions. Format: [N, H, W, C_OUT]
* @param[out] output_data Output data pointer. Data type: int8
* @return The function returns <code>ARM_CMSIS_NN_SUCCESS</code>
*
* @details
* 1. Supported framework: TensorFlow Lite micro
* 2. Additional memory is required for optimization. Refer to argument 'ctx' for details.
*
*/
arm_cmsis_nn_status arm_convolve_s4(const cmsis_nn_context *ctx,
const cmsis_nn_conv_params *conv_params,
const cmsis_nn_per_channel_quant_params *quant_params,
const cmsis_nn_dims *input_dims,
const int8_t *input_data,
const cmsis_nn_dims *filter_dims,
const int8_t *filter_data,
const cmsis_nn_dims *bias_dims,
const int32_t *bias_data,
const cmsis_nn_dims *output_dims,
int8_t *output_data);
/**
* @brief Basic s8 convolution function
* @param[in, out] ctx Function context that contains the additional buffer if required by the function.
* arm_convolve_s8_get_buffer_size will return the buffer_size if required.
* The caller is expected to clear the buffer, if applicable, for security reasons.
* @param[in] conv_params Convolution parameters (e.g. strides, dilations, pads,...).
* Range of conv_params->input_offset : [-127, 128]
* Range of conv_params->output_offset : [-128, 127]
* @param[in] quant_params Per-channel quantization info.
* It contains the multiplier and shift values to be applied to each output channel
* @param[in] input_dims Input (activation) tensor dimensions. Format: [N, H, W, C_IN]
* @param[in] input_data Input (activation) data pointer. Data type: int8
* @param[in] filter_dims Filter tensor dimensions. Format: [C_OUT, HK, WK, C_IN] where HK and WK are the
* spatial filter dimensions
* @param[in] filter_data Filter data pointer. Data type: int8
* @param[in] bias_dims Bias tensor dimensions. Format: [C_OUT]
* @param[in] bias_data Optional bias data pointer. Data type: int32
* @param[in] output_dims Output tensor dimensions. Format: [N, H, W, C_OUT]
* @param[out] output_data Output data pointer. Data type: int8
* @return The function returns <code>ARM_CMSIS_NN_SUCCESS</code>
*
* @details
* 1. Supported framework: TensorFlow Lite micro
* 2. Additional memory is required for optimization. Refer to argument 'ctx' for details.
*
*/
arm_cmsis_nn_status arm_convolve_s8(const cmsis_nn_context *ctx,
const cmsis_nn_conv_params *conv_params,
const cmsis_nn_per_channel_quant_params *quant_params,
const cmsis_nn_dims *input_dims,
const int8_t *input_data,
const cmsis_nn_dims *filter_dims,
const int8_t *filter_data,
const cmsis_nn_dims *bias_dims,
const int32_t *bias_data,
const cmsis_nn_dims *output_dims,
int8_t *output_data);
/**
* @brief Get the required buffer size for s4 convolution function
*
* @param[in] input_dims Input (activation) tensor dimensions. Format: [N, H, W, C_IN]
* @param[in] filter_dims Filter tensor dimensions. Format: [C_OUT, HK, WK, C_IN] where HK and WK
* are the spatial filter dimensions
* @return The function returns required buffer size(bytes)
*
*/
int32_t arm_convolve_s4_get_buffer_size(const cmsis_nn_dims *input_dims, const cmsis_nn_dims *filter_dims);
/**
* @brief Get the required buffer size for s8 convolution function
*
* @param[in] input_dims Input (activation) tensor dimensions. Format: [N, H, W, C_IN]
* @param[in] filter_dims Filter tensor dimensions. Format: [C_OUT, HK, WK, C_IN] where HK and WK
* are the spatial filter dimensions
* @return The function returns required buffer size(bytes)
*
*/
int32_t arm_convolve_s8_get_buffer_size(const cmsis_nn_dims *input_dims, const cmsis_nn_dims *filter_dims);
/**
* @brief Basic s8 transpose convolution function
* @param[in, out] ctx Function context that contains the additional buffer if required by the
* function.
* arm_transpose_conv_s8_get_buffer_size will return the buffer_size if required.
* The caller is expected to clear the buffer, if applicable, for security
reasons.
* @param[in, out] output_ctx Temporary scratch buffer.
* The size required size is: output width * output height * output channel * 4
* The caller is expected to clear the buffer, if applicable, for security
* reasons.
* @param[in] transpose_conv_params Convolution parameters (e.g. strides, dilations, pads,...).
* Range of transpose_conv_params->input_offset : [-127, 128]
* Range of transpose_conv_params->output_offset : [-128, 127]
* @param[in] quant_params Per-channel quantization info.
* It contains the multiplier and shift values to be applied to each out channel.
* @param[in] input_dims Input (activation) tensor dimensions. Format: [N, H, W, C_IN]
* @param[in] input_data Input (activation) data pointer. Data type: int8
* @param[in] filter_dims Filter tensor dimensions. Format: [C_OUT, HK, WK, C_IN] where HK and WK are the
* spatial filter dimensions
* @param[in] filter_data Filter data pointer. Data type: int8
* @param[in] bias_dims Bias tensor dimensions. Format: [C_OUT]
* @param[in] bias_data Optional bias data pointer. Data type: int32
* @param[in] output_dims Output tensor dimensions. Format: [N, H, W, C_OUT]
* @param[out] output_data Output data pointer. Data type: int8
* @return The function returns either
* <code>ARM_CMSIS_NN_ARG_ERROR</code> if argument constraints fail. or,
* <code>ARM_CMSIS_NN_SUCCESS</code> on successful completion.
*
* @details
* 1. Supported framework: TensorFlow Lite micro
* 2. Additional memory is required for optimization. Refer to arguments 'ctx' and 'output_ctx' for details.
*
*/
arm_cmsis_nn_status arm_transpose_conv_s8(const cmsis_nn_context *ctx,
const cmsis_nn_context *output_ctx,
const cmsis_nn_transpose_conv_params *transpose_conv_params,
const cmsis_nn_per_channel_quant_params *quant_params,
const cmsis_nn_dims *input_dims,
const int8_t *input_data,
const cmsis_nn_dims *filter_dims,
const int8_t *filter_data,
const cmsis_nn_dims *bias_dims,
const int32_t *bias_data,
const cmsis_nn_dims *output_dims,
int8_t *output_data);
/**
* @brief Get the required buffer size for s8 transpose conv function
*
* @param[in] input_dims Input (activation) tensor dimensions. Format: [N, H, W, C_IN]
* @param[in] filter_dims Filter tensor dimensions. Format: [C_OUT, HK, WK, C_IN] where HK and WK
* are the spatial filter dimensions
* @param[in] out_dims Output tensor dimensions. Format: [N, H, W, C_OUT]
* @return The function returns required buffer size(bytes)
*
*/
int32_t arm_transpose_conv_s8_get_buffer_size(const cmsis_nn_dims *input_dims,
const cmsis_nn_dims *filter_dims,
const cmsis_nn_dims *out_dims);
/**
* @brief Get size of additional buffer required by arm_transpose_conv_s8() for processors with DSP extension.
* Refer to arm_transpose_conv_s8_get_buffer_size() for function argument details.
*
* @note Intended for compilation on Host. If compiling for an Arm target, use
* arm_transpose_conv_s8_get_buffer_size().
*
*/
int32_t arm_transpose_conv_s8_get_buffer_size_dsp(const cmsis_nn_dims *input_dims,
const cmsis_nn_dims *filter_dims,
const cmsis_nn_dims *out_dims);
/**
* @brief Get size of additional buffer required by arm_transpose_conv_s8() for Arm(R) Helium Architecture case.
* Refer to arm_transpose_conv_s8_get_buffer_size() for function argument details.
*
* @note Intended for compilation on Host. If compiling for an Arm target, use
* arm_transpose_conv_s8_get_buffer_size().
*
*/
int32_t arm_transpose_conv_s8_get_buffer_size_mve(const cmsis_nn_dims *input_dims,
const cmsis_nn_dims *filter_dims,
const cmsis_nn_dims *out_dims);
/**
* @brief Basic s16 convolution function
* @param[in, out] ctx Function context that contains the additional buffer if required by the function.
* arm_convolve_s16_get_buffer_size will return the buffer_size if required.
* The caller is expected to clear the buffer, if applicable, for security reasons.
* @param[in] conv_params Convolution parameters (e.g. strides, dilations, pads,...).
* conv_params->input_offset : Not used
* conv_params->output_offset : Not used
* @param[in] quant_params Per-channel quantization info.
* It contains the multiplier and shift values to be applied to each output channel
* @param[in] input_dims Input (activation) tensor dimensions. Format: [N, H, W, C_IN]
* @param[in] input_data Input (activation) data pointer. Data type: int16
* @param[in] filter_dims Filter tensor dimensions. Format: [C_OUT, HK, WK, C_IN] where HK and WK are the
* spatial filter dimensions
* @param[in] filter_data Filter data pointer. Data type: int8
* @param[in] bias_dims Bias tensor dimensions. Format: [C_OUT]
* @param[in] bias_data Optional bias data pointer. Data type: int64
* @param[in] output_dims Output tensor dimensions. Format: [N, H, W, C_OUT]
* @param[out] output_data Output data pointer. Data type: int16
* @return The function returns <code>ARM_CMSIS_NN_SUCCESS</code>
*
* @details
* 1. Supported framework: TensorFlow Lite micro
* 2. Additional memory is required for optimization. Refer to argument 'ctx' for details.
*
*/
arm_cmsis_nn_status arm_convolve_s16(const cmsis_nn_context *ctx,
const cmsis_nn_conv_params *conv_params,
const cmsis_nn_per_channel_quant_params *quant_params,
const cmsis_nn_dims *input_dims,
const int16_t *input_data,
const cmsis_nn_dims *filter_dims,
const int8_t *filter_data,
const cmsis_nn_dims *bias_dims,
const int64_t *bias_data,
const cmsis_nn_dims *output_dims,
int16_t *output_data);
/**
* @brief Optimized s16 convolution function
* @param[in, out] ctx Function context that contains the additional buffer if required by the function.
* arm_convolve_fast_s16_get_buffer_size will return the buffer_size if required.
* The caller is expected to clear the buffer, if applicable, for security reasons.
* @param[in] conv_params Convolution parameters (e.g. strides, dilations, pads,...).
* conv_params->input_offset : Not used
* conv_params->output_offset : Not used
* @param[in] quant_params Per-channel quantization info.
* It contains the multiplier and shift values to be applied to each output channel
* @param[in] input_dims Input (activation) tensor dimensions. Format: [N, H, W, C_IN]
* @param[in] input_data Input (activation) data pointer. Data type: int16
* @param[in] filter_dims Filter tensor dimensions. Format: [C_OUT, HK, WK, C_IN] where HK and WK are the
* spatial filter dimensions. (filter_dims->w * filter_dims->h * input_dims->c) must not
exceed 512
* @param[in] filter_data Filter data pointer. Data type: int8
* @param[in] bias_dims Bias tensor dimensions. Format: [C_OUT]
* @param[in] bias_data Optional bias data pointer. Data type: int64
* @param[in] output_dims Output tensor dimensions. Format: [N, H, W, C_OUT]
* @param[out] output_data Output data pointer. Data type: int16
* @return The function returns <code>ARM_CMSIS_NN_SUCCESS</code>
*
* @details
* 1. Supported framework: TensorFlow Lite micro
* 2. Additional memory is required for optimization. Refer to argument 'ctx' for details.
* 3. Implementation supports kernel volumes (filter width * filter height * input channels) < 512.
*
*/
arm_cmsis_nn_status arm_convolve_fast_s16(const cmsis_nn_context *ctx,
const cmsis_nn_conv_params *conv_params,
const cmsis_nn_per_channel_quant_params *quant_params,
const cmsis_nn_dims *input_dims,
const int16_t *input_data,
const cmsis_nn_dims *filter_dims,
const int8_t *filter_data,
const cmsis_nn_dims *bias_dims,
const int64_t *bias_data,
const cmsis_nn_dims *output_dims,
int16_t *output_data);
/**
* @brief Get the required buffer size for s16 convolution function
*
* @param[in] input_dims Input (activation) tensor dimensions. Format: [N, H, W, C_IN]
* @param[in] filter_dims Filter tensor dimensions. Format: [C_OUT, HK, WK, C_IN] where HK and WK
* are the spatial filter dimensions
* @return The function returns required buffer size(bytes)
*
*/
int32_t arm_convolve_s16_get_buffer_size(const cmsis_nn_dims *input_dims, const cmsis_nn_dims *filter_dims);
/**
* @brief Get the required buffer size for fast s16 convolution function
*
* @param[in] input_dims Input (activation) tensor dimensions. Format: [N, H, W, C_IN]
* @param[in] filter_dims Filter tensor dimensions. Format: [C_OUT, HK, WK, C_IN] where HK and WK
* are the spatial filter dimensions
* @return The function returns required buffer size(bytes)
*
*/
int32_t arm_convolve_fast_s16_get_buffer_size(const cmsis_nn_dims *input_dims, const cmsis_nn_dims *filter_dims);
/**
* @brief Fast s4 version for 1x1 convolution (non-square shape)
*
* @param[in, out] ctx Function context that contains the additional buffer if required by the function.
* arm_convolve_1x1_s4_fast_get_buffer_size will return the buffer_size if required.
* The caller is expected to clear the buffer ,if applicable, for security reasons.
* @param[in] conv_params Convolution parameters (e.g. strides, dilations, pads,...).
* Range of conv_params->input_offset : [-127, 128]
* Range of conv_params->output_offset : [-128, 127]
* @param[in] quant_params Per-channel quantization info.
* It contains the multiplier and shift values to be applied to each output channel
* @param[in] input_dims Input (activation) tensor dimensions. Format: [N, H, W, C_IN]
* @param[in] input_data Input (activation) data pointer. Data type: int8
* @param[in] filter_dims Filter tensor dimensions. Format: [C_OUT, 1, 1, C_IN]
* @param[in] filter_data Filter data pointer. Data type: int8 packed with 2x int4
* @param[in] bias_dims Bias tensor dimensions. Format: [C_OUT]
* @param[in] bias_data Optional bias data pointer. Data type: int32
* @param[in] output_dims Output tensor dimensions. Format: [N, H, W, C_OUT]
* @param[out] output_data Output data pointer. Data type: int8
*
* @return The function returns either
* <code>ARM_CMSIS_NN_ARG_ERROR</code> if argument constraints fail. or,
* <code>ARM_CMSIS_NN_SUCCESS</code> on successful completion.
*
* @details
* - Supported framework : TensorFlow Lite Micro
* - The following constrains on the arguments apply
* -# conv_params->padding.w = conv_params->padding.h = 0
* -# conv_params->stride.w = conv_params->stride.h = 1
*
*/
arm_cmsis_nn_status arm_convolve_1x1_s4_fast(const cmsis_nn_context *ctx,
const cmsis_nn_conv_params *conv_params,
const cmsis_nn_per_channel_quant_params *quant_params,
const cmsis_nn_dims *input_dims,
const int8_t *input_data,
const cmsis_nn_dims *filter_dims,
const int8_t *filter_data,
const cmsis_nn_dims *bias_dims,
const int32_t *bias_data,
const cmsis_nn_dims *output_dims,
int8_t *output_data);
/**
* @brief s4 version for 1x1 convolution with support for non-unity stride values
*
* @param[in, out] ctx Function context that contains the additional buffer if required by the function.
* None is required by this function.
* @param[in] conv_params Convolution parameters (e.g. strides, dilations, pads,...).
* Range of conv_params->input_offset : [-127, 128]
* Range of conv_params->output_offset : [-128, 127]
* @param[in] quant_params Per-channel quantization info.
* It contains the multiplier and shift values to be applied to each output channel
* @param[in] input_dims Input (activation) tensor dimensions. Format: [N, H, W, C_IN]
* @param[in] input_data Input (activation) data pointer. Data type: int8
* @param[in] filter_dims Filter tensor dimensions. Format: [C_OUT, 1, 1, C_IN]
* @param[in] filter_data Filter data pointer. Data type: int8 packed with 2x int4
* @param[in] bias_dims Bias tensor dimensions. Format: [C_OUT]
* @param[in] bias_data Optional bias data pointer. Data type: int32
* @param[in] output_dims Output tensor dimensions. Format: [N, H, W, C_OUT]
* @param[out] output_data Output data pointer. Data type: int8
*
* @return The function returns either
* <code>ARM_CMSIS_NN_ARG_ERROR</code> if argument constraints fail. or,
* <code>ARM_CMSIS_NN_SUCCESS</code> on successful completion.
* @details
* - Supported framework : TensorFlow Lite Micro
* - The following constrains on the arguments apply
* -# conv_params->padding.w = conv_params->padding.h = 0
*
*/
arm_cmsis_nn_status arm_convolve_1x1_s4(const cmsis_nn_context *ctx,
const cmsis_nn_conv_params *conv_params,
const cmsis_nn_per_channel_quant_params *quant_params,
const cmsis_nn_dims *input_dims,
const int8_t *input_data,
const cmsis_nn_dims *filter_dims,
const int8_t *filter_data,
const cmsis_nn_dims *bias_dims,
const int32_t *bias_data,
const cmsis_nn_dims *output_dims,
int8_t *output_data);
/**
* @brief Fast s8 version for 1x1 convolution (non-square shape)
*
* @param[in, out] ctx Function context that contains the additional buffer if required by the function.
* arm_convolve_1x1_s8_fast_get_buffer_size will return the buffer_size if required.
* The caller is expected to clear the buffer, if applicable, for security reasons.
* @param[in] conv_params Convolution parameters (e.g. strides, dilations, pads,...).
* Range of conv_params->input_offset : [-127, 128]
* Range of conv_params->output_offset : [-128, 127]
* @param[in] quant_params Per-channel quantization info.
* It contains the multiplier and shift values to be applied to each output channel
* @param[in] input_dims Input (activation) tensor dimensions. Format: [N, H, W, C_IN]
* @param[in] input_data Input (activation) data pointer. Data type: int8
* @param[in] filter_dims Filter tensor dimensions. Format: [C_OUT, 1, 1, C_IN]
* @param[in] filter_data Filter data pointer. Data type: int8
* @param[in] bias_dims Bias tensor dimensions. Format: [C_OUT]
* @param[in] bias_data Optional bias data pointer. Data type: int32
* @param[in] output_dims Output tensor dimensions. Format: [N, H, W, C_OUT]
* @param[out] output_data Output data pointer. Data type: int8
*
* @return The function returns either
* <code>ARM_CMSIS_NN_ARG_ERROR</code> if argument constraints fail. or,
* <code>ARM_CMSIS_NN_SUCCESS</code> on successful completion.
*
* @details
* - Supported framework : TensorFlow Lite Micro
* - The following constrains on the arguments apply
* -# conv_params->padding.w = conv_params->padding.h = 0
* -# conv_params->stride.w = conv_params->stride.h = 1
*
*/
arm_cmsis_nn_status arm_convolve_1x1_s8_fast(const cmsis_nn_context *ctx,
const cmsis_nn_conv_params *conv_params,
const cmsis_nn_per_channel_quant_params *quant_params,
const cmsis_nn_dims *input_dims,
const int8_t *input_data,
const cmsis_nn_dims *filter_dims,
const int8_t *filter_data,
const cmsis_nn_dims *bias_dims,
const int32_t *bias_data,
const cmsis_nn_dims *output_dims,
int8_t *output_data);
/**
* @brief Get the required buffer size for arm_convolve_1x1_s4_fast
*
* @param[in] input_dims Input (activation) dimensions
* @return The function returns the required buffer size in bytes
*
*/
int32_t arm_convolve_1x1_s4_fast_get_buffer_size(const cmsis_nn_dims *input_dims);
/**
* @brief Get the required buffer size for arm_convolve_1x1_s8_fast
*
* @param[in] input_dims Input (activation) dimensions
* @return The function returns the required buffer size in bytes
*
*/
int32_t arm_convolve_1x1_s8_fast_get_buffer_size(const cmsis_nn_dims *input_dims);
/**
* @brief s8 version for 1x1 convolution with support for non-unity stride values
*
* @param[in, out] ctx Function context that contains the additional buffer if required by the function.
* None is required by this function.
* @param[in] conv_params Convolution parameters (e.g. strides, dilations, pads,...).
* Range of conv_params->input_offset : [-127, 128]
* Range of conv_params->output_offset : [-128, 127]
* @param[in] quant_params Per-channel quantization info.
* It contains the multiplier and shift values to be applied to each output channel
* @param[in] input_dims Input (activation) tensor dimensions. Format: [N, H, W, C_IN]
* @param[in] input_data Input (activation) data pointer. Data type: int8
* @param[in] filter_dims Filter tensor dimensions. Format: [C_OUT, 1, 1, C_IN]
* @param[in] filter_data Filter data pointer. Data type: int8
* @param[in] bias_dims Bias tensor dimensions. Format: [C_OUT]
* @param[in] bias_data Optional bias data pointer. Data type: int32
* @param[in] output_dims Output tensor dimensions. Format: [N, H, W, C_OUT]
* @param[out] output_data Output data pointer. Data type: int8
*
* @return The function returns either
* <code>ARM_CMSIS_NN_ARG_ERROR</code> if argument constraints fail. or,
* <code>ARM_CMSIS_NN_SUCCESS</code> on successful completion.
* @details
* - Supported framework : TensorFlow Lite Micro
* - The following constrains on the arguments apply
* -# conv_params->padding.w = conv_params->padding.h = 0
*
*/
arm_cmsis_nn_status arm_convolve_1x1_s8(const cmsis_nn_context *ctx,
const cmsis_nn_conv_params *conv_params,
const cmsis_nn_per_channel_quant_params *quant_params,
const cmsis_nn_dims *input_dims,
const int8_t *input_data,
const cmsis_nn_dims *filter_dims,
const int8_t *filter_data,
const cmsis_nn_dims *bias_dims,
const int32_t *bias_data,
const cmsis_nn_dims *output_dims,
int8_t *output_data);
/**
* @brief 1xn convolution
*
* @param[in, out] ctx Function context that contains the additional buffer if required by the function.
* arm_convolve_1_x_n_s8_get_buffer_size will return the buffer_size if required
* The caller is expected to clear the buffer, if applicable, for security reasons.
* @param[in] conv_params Convolution parameters (e.g. strides, dilations, pads,...).
* Range of conv_params->input_offset : [-127, 128]
* Range of conv_params->output_offset : [-128, 127]
* @param[in] quant_params Per-channel quantization info.
* It contains the multiplier and shift values to be applied to each output channel
* @param[in] input_dims Input (activation) tensor dimensions. Format: [N, H, W, C_IN]
* @param[in] input_data Input (activation) data pointer. Data type: int8
* @param[in] filter_dims Filter tensor dimensions. Format: [C_OUT, 1, WK, C_IN] where WK is the horizontal
* spatial filter dimension
* @param[in] filter_data Filter data pointer. Data type: int8
* @param[in] bias_dims Bias tensor dimensions. Format: [C_OUT]
* @param[in] bias_data Optional bias data pointer. Data type: int32
* @param[in] output_dims Output tensor dimensions. Format: [N, H, W, C_OUT]
* @param[out] output_data Output data pointer. Data type: int8
*
* @return The function returns either
* <code>ARM_CMSIS_NN_ARG_ERROR</code> if argument constraints fail. or,
* <code>ARM_CMSIS_NN_SUCCESS</code> on successful completion.
*
* @details
* - Supported framework : TensorFlow Lite Micro
* - The following constrains on the arguments apply
* -# input_dims->n equals 1
* -# ouput_dims->w is a multiple of 4
* -# Explicit constraints(since it is for 1xN convolution)
* -## input_dims->h equals 1
* -## output_dims->h equals 1
* -## filter_dims->h equals 1
*@todo Remove constraint on output_dims->w to make the function generic.
*
*/
arm_cmsis_nn_status arm_convolve_1_x_n_s8(const cmsis_nn_context *ctx,
const cmsis_nn_conv_params *conv_params,
const cmsis_nn_per_channel_quant_params *quant_params,
const cmsis_nn_dims *input_dims,
const int8_t *input_data,
const cmsis_nn_dims *filter_dims,
const int8_t *filter_data,
const cmsis_nn_dims *bias_dims,
const int32_t *bias_data,
const cmsis_nn_dims *output_dims,
int8_t *output_data);
/**
* @brief Get the required additional buffer size for 1xn convolution
*
* @param[in] input_dims Input (activation) tensor dimensions. Format: [N, H, W, C_IN]
* @param[in] filter_dims Filter tensor dimensions. Format: [C_OUT, 1, WK, C_IN] where WK is the
* horizontal spatial filter dimension
* @return The function returns required buffer size(bytes)
*
*/
int32_t arm_convolve_1_x_n_s8_get_buffer_size(const cmsis_nn_dims *input_dims, const cmsis_nn_dims *filter_dims);
/**
* @brief Wrapper function to pick the right optimized s8 depthwise convolution function
*
* @param[in, out] ctx Function context (e.g. temporary buffer). Check the function
* definition file to see if an additional buffer is required.
* Optional function {API}_get_buffer_size() provides the buffer
* size if required.
* The caller is expected to clear the buffer, if applicable, for security reasons.
* @param[in] dw_conv_params Depthwise convolution parameters (e.g. strides, dilations, pads,...)
* dw_conv_params->dilation is not used.
* Range of dw_conv_params->input_offset : [-127, 128]
* Range of dw_conv_params->output_offset : [-128, 127]
* @param[in] quant_params Per-channel quantization info.
* It contains the multiplier and shift values to be applied to each
* output channel
* @param[in] input_dims Input (activation) tensor dimensions. Format: [H, W, C_IN]
* Batch argument N is not used and assumed to be 1.
* @param[in] input_data Input (activation) data pointer. Data type: int8
* @param[in] filter_dims Filter tensor dimensions. Format: [1, H, W, C_OUT]
* @param[in] filter_data Filter data pointer. Data type: int8
* @param[in] bias_dims Bias tensor dimensions. Format: [C_OUT]
* @param[in] bias_data Bias data pointer. Data type: int32
* @param[in] output_dims Output tensor dimensions. Format: [1, H, W, C_OUT]
* @param[in, out] output_data Output data pointer. Data type: int8
* @return The function returns
* <code>ARM_CMSIS_NN_SUCCESS</code> - Successful completion.
*
* @details
* - Supported framework: TensorFlow Lite
* - Picks one of the the following functions
* -# arm_depthwise_conv_s8()
* -# arm_depthwise_conv_3x3_s8() - Cortex-M CPUs with DSP extension only
* -# arm_depthwise_conv_s8_opt()
* - Check details of arm_depthwise_conv_s8_opt() for potential data that can be accessed outside of the
* boundary.
*/
arm_cmsis_nn_status arm_depthwise_conv_wrapper_s8(const cmsis_nn_context *ctx,
const cmsis_nn_dw_conv_params *dw_conv_params,
const cmsis_nn_per_channel_quant_params *quant_params,
const cmsis_nn_dims *input_dims,
const int8_t *input_data,
const cmsis_nn_dims *filter_dims,
const int8_t *filter_data,
const cmsis_nn_dims *bias_dims,
const int32_t *bias_data,
const cmsis_nn_dims *output_dims,
int8_t *output_data);
/**
* @brief Wrapper function to pick the right optimized s4 depthwise convolution function
*
* @param[in, out] ctx Function context (e.g. temporary buffer). Check the function
* definition file to see if an additional buffer is required.
* Optional function {API}_get_buffer_size() provides the buffer
* size if required.
* The caller is expected to clear the buffer ,if applicable, for security reasons.
* @param[in] dw_conv_params Depthwise convolution parameters (e.g. strides, dilations, pads,...)
* dw_conv_params->dilation is not used.
* Range of dw_conv_params->input_offset : [-127, 128]
* Range of dw_conv_params->output_offset : [-128, 127]
* @param[in] quant_params Per-channel quantization info.
* It contains the multiplier and shift values to be applied to each
* output channel
* @param[in] input_dims Input (activation) tensor dimensions. Format: [H, W, C_IN]
* Batch argument N is not used and assumed to be 1.
* @param[in] input_data Input (activation) data pointer. Data type: int8
* @param[in] filter_dims Filter tensor dimensions. Format: [1, H, W, C_OUT]
* @param[in] filter_data Filter data pointer. Data type: int8_t packed 4-bit weights, e.g four sequential
* weights [0x1, 0x2, 0x3, 0x4] packed as [0x21, 0x43].
* @param[in] bias_dims Bias tensor dimensions. Format: [C_OUT]
* @param[in] bias_data Bias data pointer. Data type: int32
* @param[in] output_dims Output tensor dimensions. Format: [1, H, W, C_OUT]
* @param[in, out] output_data Output data pointer. Data type: int8
* @return The function returns
* <code>ARM_CMSIS_NN_SUCCESS</code> - Successful completion.
*