| 1 | /*
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| 2 | * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
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| 3 | *
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| 4 | * SPDX-License-Identifier: Apache-2.0
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| 5 | *
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| 6 | * Licensed under the Apache License, Version 2.0 (the License); you may
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| 7 | * not use this file except in compliance with the License.
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| 8 | * You may obtain a copy of the License at
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| 9 | *
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| 10 | * www.apache.org/licenses/LICENSE-2.0
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| 11 | *
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| 12 | * Unless required by applicable law or agreed to in writing, software
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| 13 | * distributed under the License is distributed on an AS IS BASIS, WITHOUT
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| 14 | * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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| 15 | * See the License for the specific language governing permissions and
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| 16 | * limitations under the License.
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| 17 | */
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| 18 |
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| 19 | /* ----------------------------------------------------------------------
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| 20 | * Project: CMSIS NN Library
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| 21 | * Title: arm_nnfunctions.h
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| 22 | * Description: Public header file for CMSIS NN Library
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| 23 | *
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| 24 | * $Date: 13. July 2018
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| 25 | * $Revision: V.1.0.0
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| 26 | *
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| 27 | * Target Processor: Cortex-M cores
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| 28 | * -------------------------------------------------------------------- */
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| 29 |
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| 30 | /**
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| 31 | \mainpage CMSIS NN Software Library
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| 32 | *
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| 33 | * Introduction
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| 34 | * ------------
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| 35 | *
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| 36 | * This user manual describes the CMSIS NN software library,
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| 37 | * a collection of efficient neural network kernels developed to maximize the
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| 38 | * performance and minimize the memory footprint of neural networks on Cortex-M processor cores.
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| 39 | *
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| 40 | * The library is divided into a number of functions each covering a specific category:
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| 41 | * - Neural Network Convolution Functions
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| 42 | * - Neural Network Activation Functions
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| 43 | * - Fully-connected Layer Functions
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| 44 | * - Neural Network Pooling Functions
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| 45 | * - Softmax Functions
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| 46 | * - Neural Network Support Functions
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| 47 | *
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| 48 | * The library has separate functions for operating on different weight and activation data
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| 49 | * types including 8-bit integers (q7_t) and 16-bit integers (q15_t). The descrition of the
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| 50 | * kernels are included in the function description. The implementation details are also
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| 51 | * described in this paper [1].
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| 52 | *
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| 53 | * Block Diagram
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| 54 | * --------
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| 55 | * \image html CMSIS-NN-OVERVIEW.PNG
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| 56 | *
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| 57 | * Examples
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| 58 | * --------
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| 59 | *
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| 60 | * The library ships with a number of examples which demonstrate how to use the library functions.
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| 61 | *
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| 62 | * Pre-processor Macros
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| 63 | * ------------
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| 64 | *
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| 65 | * Each library project have differant pre-processor macros.
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| 66 | *
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| 67 | * - ARM_MATH_DSP:
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| 68 | *
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| 69 | * Define macro ARM_MATH_DSP, If the silicon supports DSP instructions.
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| 70 | *
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| 71 | * - ARM_MATH_BIG_ENDIAN:
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| 72 | *
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| 73 | * Define macro ARM_MATH_BIG_ENDIAN to build the library for big endian targets. By default library builds for little endian targets.
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| 74 | *
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| 75 | * - ARM_NN_TRUNCATE:
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| 76 | *
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| 77 | * Define macro ARM_NN_TRUNCATE to use floor instead of round-to-the-nearest-int for the computation.
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| 78 | *
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| 79 | * Copyright Notice
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| 80 | * ------------
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| 81 | *
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| 82 | * Copyright (C) 2010-2018 Arm Limited. All rights reserved.
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| 83 | *
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| 84 | * [1] CMSIS-NN: Efficient Neural Network Kernels for Arm Cortex-M CPUs https://arxiv.org/abs/1801.06601
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| 85 | */
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| 86 |
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| 87 | /**
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| 88 | * @defgroup groupNN Neural Network Functions
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| 89 | * These functions perform basic operations for neural network layers.
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| 90 | */
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| 91 |
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| 92 | #ifndef _ARM_NNFUNCTIONS_H
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| 93 | #define _ARM_NNFUNCTIONS_H
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| 94 |
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| 95 | #include "arm_nnsupportfunctions.h"
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| 96 | #include "arm_nn_tables.h"
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| 97 |
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| 98 | #define USE_INTRINSIC
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| 99 |
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| 100 | //#define ARM_NN_TRUNCATE /* This config the rounding model to floor or round to the nearest int */
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| 101 |
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| 102 | #ifdef __cplusplus
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| 103 | extern "C"
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| 104 | {
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| 105 | #endif
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| 106 |
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| 107 | /**
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| 108 | * @defgroup NNConv Neural Network Convolution Functions
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| 109 | *
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| 110 | * Perform convolution layer
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| 111 | *
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| 112 | * The convolution is implemented in 2 steps: im2col and GEMM
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| 113 | *
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| 114 | * im2col is a process of converting each patch of image data into
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| 115 | * a column. After im2col, the convolution is computed as matrix-matrix
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| 116 | * multiplication.
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| 117 | *
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| 118 | * To reduce the memory footprint, the im2col is performed partially.
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| 119 | * Each iteration, only a few column (i.e., patches) are generated and
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| 120 | * computed with GEMM kernels similar to CMSIS-DSP arm_mat_mult functions.
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| 121 | *
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| 122 | */
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| 123 |
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| 124 | /**
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| 125 | * @brief Basic Q7 convolution function
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| 126 | * @param[in] Im_in pointer to input tensor
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| 127 | * @param[in] dim_im_in input tensor dimention
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| 128 | * @param[in] ch_im_in number of input tensor channels
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| 129 | * @param[in] wt pointer to kernel weights
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| 130 | * @param[in] ch_im_out number of filters, i.e., output tensor channels
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| 131 | * @param[in] dim_kernel filter kernel size
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| 132 | * @param[in] padding padding sizes
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| 133 | * @param[in] stride convolution stride
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| 134 | * @param[in] bias pointer to bias
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| 135 | * @param[in] bias_shift amount of left-shift for bias
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| 136 | * @param[in] out_shift amount of right-shift for output
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| 137 | * @param[in,out] Im_out pointer to output tensor
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| 138 | * @param[in] dim_im_out output tensor dimension
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| 139 | * @param[in,out] bufferA pointer to buffer space for input
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| 140 | * @param[in,out] bufferB pointer to buffer space for output
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| 141 | * @return The function returns <code>ARM_MATH_SUCCESS</code>
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| 142 | *
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| 143 | */
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| 144 |
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| 145 | arm_status arm_convolve_HWC_q7_basic(const q7_t * Im_in,
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| 146 | const uint16_t dim_im_in,
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| 147 | const uint16_t ch_im_in,
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| 148 | const q7_t * wt,
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| 149 | const uint16_t ch_im_out,
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| 150 | const uint16_t dim_kernel,
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| 151 | const uint16_t padding,
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| 152 | const uint16_t stride,
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| 153 | const q7_t * bias,
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| 154 | const uint16_t bias_shift,
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| 155 | const uint16_t out_shift,
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| 156 | q7_t * Im_out,
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| 157 | const uint16_t dim_im_out,
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| 158 | q15_t * bufferA,
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| 159 | q7_t * bufferB);
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| 160 |
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| 161 | /**
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| 162 | * @brief Basic Q7 convolution function (non-sqaure shape)
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| 163 | * @param[in] Im_in pointer to input tensor
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| 164 | * @param[in] dim_im_in_x input tensor dimention x
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| 165 | * @param[in] dim_im_in_y input tensor dimention y
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| 166 | * @param[in] ch_im_in number of input tensor channels
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| 167 | * @param[in] wt pointer to kernel weights
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| 168 | * @param[in] ch_im_out number of filters, i.e., output tensor channels
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| 169 | * @param[in] dim_kernel_x filter kernel size x
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| 170 | * @param[in] dim_kernel_y filter kernel size y
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| 171 | * @param[in] padding_x padding size x
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| 172 | * @param[in] padding_y padding size y
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| 173 | * @param[in] stride_x convolution stride x
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| 174 | * @param[in] stride_y convolution stride y
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| 175 | * @param[in] bias pointer to bias
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| 176 | * @param[in] bias_shift amount of left-shift for bias
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| 177 | * @param[in] out_shift amount of right-shift for output
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| 178 | * @param[in,out] Im_out pointer to output tensor
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| 179 | * @param[in] dim_im_out_x output tensor dimension x
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| 180 | * @param[in] dim_im_out_y output tensor dimension y
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| 181 | * @param[in,out] bufferA pointer to buffer space for input
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| 182 | * @param[in,out] bufferB pointer to buffer space for output
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| 183 | * @return The function returns <code>ARM_MATH_SUCCESS</code>
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| 184 | */
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| 185 |
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| 186 | arm_status arm_convolve_HWC_q7_basic_nonsquare(const q7_t * Im_in,
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| 187 | const uint16_t dim_im_in_x,
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| 188 | const uint16_t dim_im_in_y,
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| 189 | const uint16_t ch_im_in,
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| 190 | const q7_t * wt,
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| 191 | const uint16_t ch_im_out,
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| 192 | const uint16_t dim_kernel_x,
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| 193 | const uint16_t dim_kernel_y,
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| 194 | const uint16_t padding_x,
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| 195 | const uint16_t padding_y,
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| 196 | const uint16_t stride_x,
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| 197 | const uint16_t stride_y,
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| 198 | const q7_t * bias,
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| 199 | const uint16_t bias_shift,
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| 200 | const uint16_t out_shift,
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| 201 | q7_t * Im_out,
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| 202 | const uint16_t dim_im_out_x,
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| 203 | const uint16_t dim_im_out_y,
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| 204 | q15_t * bufferA,
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| 205 | q7_t * bufferB);
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| 206 |
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| 207 | /**
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| 208 | * @brief Basic Q15 convolution function
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| 209 | * @param[in] Im_in pointer to input tensor
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| 210 | * @param[in] dim_im_in input tensor dimention
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| 211 | * @param[in] ch_im_in number of input tensor channels
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| 212 | * @param[in] wt pointer to kernel weights
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| 213 | * @param[in] ch_im_out number of filters, i.e., output tensor channels
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| 214 | * @param[in] dim_kernel filter kernel size
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| 215 | * @param[in] padding padding sizes
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| 216 | * @param[in] stride convolution stride
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| 217 | * @param[in] bias pointer to bias
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| 218 | * @param[in] bias_shift amount of left-shift for bias
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| 219 | * @param[in] out_shift amount of right-shift for output
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| 220 | * @param[in,out] Im_out pointer to output tensor
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| 221 | * @param[in] dim_im_out output tensor dimension
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| 222 | * @param[in,out] bufferA pointer to buffer space for input
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| 223 | * @param[in,out] bufferB pointer to buffer space for output
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| 224 | * @return The function returns <code>ARM_MATH_SUCCESS</code>
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| 225 | *
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| 226 | */
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| 227 |
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| 228 | arm_status arm_convolve_HWC_q15_basic(const q15_t * Im_in,
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| 229 | const uint16_t dim_im_in,
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| 230 | const uint16_t ch_im_in,
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| 231 | const q15_t * wt,
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| 232 | const uint16_t ch_im_out,
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| 233 | const uint16_t dim_kernel,
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| 234 | const uint16_t padding,
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| 235 | const uint16_t stride,
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| 236 | const q15_t * bias,
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| 237 | const uint16_t bias_shift,
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| 238 | const uint16_t out_shift,
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| 239 | q15_t * Im_out,
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| 240 | const uint16_t dim_im_out,
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| 241 | q15_t * bufferA,
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| 242 | q7_t * bufferB);
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| 243 |
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| 244 | /**
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| 245 | * @brief Fast Q7 convolution function
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| 246 | * @param[in] Im_in pointer to input tensor
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| 247 | * @param[in] dim_im_in input tensor dimention
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| 248 | * @param[in] ch_im_in number of input tensor channels
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| 249 | * @param[in] wt pointer to kernel weights
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| 250 | * @param[in] ch_im_out number of filters, i.e., output tensor channels
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| 251 | * @param[in] dim_kernel filter kernel size
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| 252 | * @param[in] padding padding sizes
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| 253 | * @param[in] stride convolution stride
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| 254 | * @param[in] bias pointer to bias
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| 255 | * @param[in] bias_shift amount of left-shift for bias
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| 256 | * @param[in] out_shift amount of right-shift for output
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| 257 | * @param[in,out] Im_out pointer to output tensor
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| 258 | * @param[in] dim_im_out output tensor dimension
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| 259 | * @param[in,out] bufferA pointer to buffer space for input
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| 260 | * @param[in,out] bufferB pointer to buffer space for output
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| 261 | * @return The function returns either
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| 262 | * <code>ARM_MATH_SIZE_MISMATCH</code> or <code>ARM_MATH_SUCCESS</code> based on the outcome of size checking.
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| 263 | *
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| 264 | * This function is the version with full list of optimization tricks, but with
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| 265 | * some contraints:
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| 266 | * ch_im_in is multiple of 4
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| 267 | * ch_im_out is multiple of 2
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| 268 | */
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| 269 |
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| 270 | arm_status arm_convolve_HWC_q7_fast(const q7_t * Im_in,
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| 271 | const uint16_t dim_im_in,
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| 272 | const uint16_t ch_im_in,
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| 273 | const q7_t * wt,
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| 274 | const uint16_t ch_im_out,
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| 275 | const uint16_t dim_kernel,
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| 276 | const uint16_t padding,
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| 277 | const uint16_t stride,
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| 278 | const q7_t * bias,
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| 279 | const uint16_t bias_shift,
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| 280 | const uint16_t out_shift,
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| 281 | q7_t * Im_out,
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| 282 | const uint16_t dim_im_out,
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| 283 | q15_t * bufferA,
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| 284 | q7_t * bufferB);
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| 285 |
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| 286 | /**
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| 287 | * @brief Fast Q7 convolution function (non-sqaure shape)
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| 288 | * @param[in] Im_in pointer to input tensor
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| 289 | * @param[in] dim_im_in_x input tensor dimention x
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| 290 | * @param[in] dim_im_in_y input tensor dimention y
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| 291 | * @param[in] ch_im_in number of input tensor channels
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| 292 | * @param[in] wt pointer to kernel weights
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| 293 | * @param[in] ch_im_out number of filters, i.e., output tensor channels
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| 294 | * @param[in] dim_kernel_x filter kernel size x
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| 295 | * @param[in] dim_kernel_y filter kernel size y
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| 296 | * @param[in] padding_x padding size x
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| 297 | * @param[in] padding_y padding size y
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| 298 | * @param[in] stride_x convolution stride x
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| 299 | * @param[in] stride_y convolution stride y
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| 300 | * @param[in] bias pointer to bias
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| 301 | * @param[in] bias_shift amount of left-shift for bias
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| 302 | * @param[in] out_shift amount of right-shift for output
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| 303 | * @param[in,out] Im_out pointer to output tensor
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| 304 | * @param[in] dim_im_out_x output tensor dimension x
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| 305 | * @param[in] dim_im_out_y output tensor dimension y
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| 306 | * @param[in,out] bufferA pointer to buffer space for input
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| 307 | * @param[in,out] bufferB pointer to buffer space for output
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| 308 | * @return The function returns either
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| 309 | * <code>ARM_MATH_SIZE_MISMATCH</code> or <code>ARM_MATH_SUCCESS</code> based on the outcome of size checking.
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| 310 | *
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| 311 | * This function is the version with full list of optimization tricks, but with
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| 312 | * some contraints:
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| 313 | * ch_im_in is multiple of 4
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| 314 | * ch_im_out is multiple of 2
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| 315 | */
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| 316 |
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| 317 | arm_status arm_convolve_HWC_q7_fast_nonsquare(const q7_t * Im_in,
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| 318 | const uint16_t dim_im_in_x,
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| 319 | const uint16_t dim_im_in_y,
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| 320 | const uint16_t ch_im_in,
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| 321 | const q7_t * wt,
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| 322 | const uint16_t ch_im_out,
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| 323 | const uint16_t dim_kernel_x,
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| 324 | const uint16_t dim_kernel_y,
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| 325 | const uint16_t padding_x,
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| 326 | const uint16_t padding_y,
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| 327 | const uint16_t stride_x,
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| 328 | const uint16_t stride_y,
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| 329 | const q7_t * bias,
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| 330 | const uint16_t bias_shift,
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| 331 | const uint16_t out_shift,
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| 332 | q7_t * Im_out,
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| 333 | const uint16_t dim_im_out_x,
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| 334 | const uint16_t dim_im_out_y,
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| 335 | q15_t * bufferA,
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| 336 | q7_t * bufferB);
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| 337 |
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| 338 | /**
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| 339 | * @brief Fast Q7 version of 1x1 convolution (non-sqaure shape)
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| 340 | * @param[in] Im_in pointer to input tensor
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| 341 | * @param[in] dim_im_in_x input tensor dimention x
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| 342 | * @param[in] dim_im_in_y input tensor dimention y
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| 343 | * @param[in] ch_im_in number of input tensor channels
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| 344 | * @param[in] wt pointer to kernel weights
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| 345 | * @param[in] ch_im_out number of filters, i.e., output tensor channels
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| 346 | * @param[in] dim_kernel_x filter kernel size x
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| 347 | * @param[in] dim_kernel_y filter kernel size y
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| 348 | * @param[in] padding_x padding size x
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| 349 | * @param[in] padding_y padding size y
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| 350 | * @param[in] stride_x convolution stride x
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| 351 | * @param[in] stride_y convolution stride y
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| 352 | * @param[in] bias pointer to bias
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| 353 | * @param[in] bias_shift amount of left-shift for bias
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| 354 | * @param[in] out_shift amount of right-shift for output
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| 355 | * @param[in,out] Im_out pointer to output tensor
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| 356 | * @param[in] dim_im_out_x output tensor dimension x
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| 357 | * @param[in] dim_im_out_y output tensor dimension y
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| 358 | * @param[in,out] bufferA pointer to buffer space for input
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| 359 | * @param[in,out] bufferB pointer to buffer space for output
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| 360 | * @return The function returns either
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| 361 | * <code>ARM_MATH_SIZE_MISMATCH</code> or <code>ARM_MATH_SUCCESS</code> based on the outcome of size checking.
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| 362 | *
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| 363 | * This function implement convolution with 1x1 kernel size (i.e., dim_kernel_x=1
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| 364 | * and dim_kernel_y=1). It can be used for
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| 365 | * second half of MobileNets after depthwise separable convolution.
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| 366 | *
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| 367 | * This function is the version with full list of optimization tricks, but with
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| 368 | * some contraints:
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| 369 | * ch_im_in is multiple of 4
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| 370 | * ch_im_out is multiple of 2
|
|---|
| 371 | */
|
|---|
| 372 | arm_status arm_convolve_1x1_HWC_q7_fast_nonsquare(const q7_t * Im_in,
|
|---|
| 373 | const uint16_t dim_im_in_x,
|
|---|
| 374 | const uint16_t dim_im_in_y,
|
|---|
| 375 | const uint16_t ch_im_in,
|
|---|
| 376 | const q7_t * wt,
|
|---|
| 377 | const uint16_t ch_im_out,
|
|---|
| 378 | const uint16_t dim_kernel_x,
|
|---|
| 379 | const uint16_t dim_kernel_y,
|
|---|
| 380 | const uint16_t padding_x,
|
|---|
| 381 | const uint16_t padding_y,
|
|---|
| 382 | const uint16_t stride_x,
|
|---|
| 383 | const uint16_t stride_y,
|
|---|
| 384 | const q7_t * bias,
|
|---|
| 385 | const uint16_t bias_shift,
|
|---|
| 386 | const uint16_t out_shift,
|
|---|
| 387 | q7_t * Im_out,
|
|---|
| 388 | const uint16_t dim_im_out_x,
|
|---|
| 389 | const uint16_t dim_im_out_y,
|
|---|
| 390 | q15_t * bufferA,
|
|---|
| 391 | q7_t * bufferB);
|
|---|
| 392 |
|
|---|
| 393 | /**
|
|---|
| 394 | * @brief Q7 version of convolution for RGB image
|
|---|
| 395 | * @param[in] Im_in pointer to input tensor
|
|---|
| 396 | * @param[in] dim_im_in input tensor dimention
|
|---|
| 397 | * @param[in] ch_im_in number of input tensor channels
|
|---|
| 398 | * @param[in] wt pointer to kernel weights
|
|---|
| 399 | * @param[in] ch_im_out number of filters, i.e., output tensor channels
|
|---|
| 400 | * @param[in] dim_kernel filter kernel size
|
|---|
| 401 | * @param[in] padding padding sizes
|
|---|
| 402 | * @param[in] stride convolution stride
|
|---|
| 403 | * @param[in] bias pointer to bias
|
|---|
| 404 | * @param[in] bias_shift amount of left-shift for bias
|
|---|
| 405 | * @param[in] out_shift amount of right-shift for output
|
|---|
| 406 | * @param[in,out] Im_out pointer to output tensor
|
|---|
| 407 | * @param[in] dim_im_out output tensor dimension
|
|---|
| 408 | * @param[in,out] bufferA pointer to buffer space for input
|
|---|
| 409 | * @param[in,out] bufferB pointer to buffer space for output
|
|---|
| 410 | * @return The function returns either
|
|---|
| 411 | * <code>ARM_MATH_SIZE_MISMATCH</code> or <code>ARM_MATH_SUCCESS</code> based on the outcome of size checking.
|
|---|
| 412 | *
|
|---|
| 413 | * This kernel is written exclusively for convolution with ch_im_in
|
|---|
| 414 | * equals 3. This applies on the first layer of CNNs which has input
|
|---|
| 415 | * image with RGB format.
|
|---|
| 416 | */
|
|---|
| 417 |
|
|---|
| 418 | arm_status arm_convolve_HWC_q7_RGB(const q7_t * Im_in,
|
|---|
| 419 | const uint16_t dim_im_in,
|
|---|
| 420 | const uint16_t ch_im_in,
|
|---|
| 421 | const q7_t * wt,
|
|---|
| 422 | const uint16_t ch_im_out,
|
|---|
| 423 | const uint16_t dim_kernel,
|
|---|
| 424 | const uint16_t padding,
|
|---|
| 425 | const uint16_t stride,
|
|---|
| 426 | const q7_t * bias,
|
|---|
| 427 | const uint16_t bias_shift,
|
|---|
| 428 | const uint16_t out_shift,
|
|---|
| 429 | q7_t * Im_out,
|
|---|
| 430 | const uint16_t dim_im_out,
|
|---|
| 431 | q15_t * bufferA,
|
|---|
| 432 | q7_t * bufferB);
|
|---|
| 433 |
|
|---|
| 434 | /**
|
|---|
| 435 | * @brief Fast Q15 convolution function
|
|---|
| 436 | * @param[in] Im_in pointer to input tensor
|
|---|
| 437 | * @param[in] dim_im_in input tensor dimention
|
|---|
| 438 | * @param[in] ch_im_in number of input tensor channels
|
|---|
| 439 | * @param[in] wt pointer to kernel weights
|
|---|
| 440 | * @param[in] ch_im_out number of filters, i.e., output tensor channels
|
|---|
| 441 | * @param[in] dim_kernel filter kernel size
|
|---|
| 442 | * @param[in] padding padding sizes
|
|---|
| 443 | * @param[in] stride convolution stride
|
|---|
| 444 | * @param[in] bias pointer to bias
|
|---|
| 445 | * @param[in] bias_shift amount of left-shift for bias
|
|---|
| 446 | * @param[in] out_shift amount of right-shift for output
|
|---|
| 447 | * @param[in,out] Im_out pointer to output tensor
|
|---|
| 448 | * @param[in] dim_im_out output tensor dimension
|
|---|
| 449 | * @param[in,out] bufferA pointer to buffer space for input
|
|---|
| 450 | * @param[in,out] bufferB pointer to buffer space for output
|
|---|
| 451 | * @return The function returns either
|
|---|
| 452 | * <code>ARM_MATH_SIZE_MISMATCH</code> or <code>ARM_MATH_SUCCESS</code> based on the outcome of size checking.
|
|---|
| 453 | *
|
|---|
| 454 | * This function is the version with full list of optimization tricks, but with
|
|---|
| 455 | * some contraints:
|
|---|
| 456 | * ch_im_in is multiple of 2
|
|---|
| 457 | * ch_im_out is multiple of 2
|
|---|
| 458 | */
|
|---|
| 459 |
|
|---|
| 460 | arm_status arm_convolve_HWC_q15_fast(const q15_t * Im_in,
|
|---|
| 461 | const uint16_t dim_im_in,
|
|---|
| 462 | const uint16_t ch_im_in,
|
|---|
| 463 | const q15_t * wt,
|
|---|
| 464 | const uint16_t ch_im_out,
|
|---|
| 465 | const uint16_t dim_kernel,
|
|---|
| 466 | const uint16_t padding,
|
|---|
| 467 | const uint16_t stride,
|
|---|
| 468 | const q15_t * bias,
|
|---|
| 469 | const uint16_t bias_shift,
|
|---|
| 470 | const uint16_t out_shift,
|
|---|
| 471 | q15_t * Im_out,
|
|---|
| 472 | const uint16_t dim_im_out,
|
|---|
| 473 | q15_t * bufferA,
|
|---|
| 474 | q7_t * bufferB);
|
|---|
| 475 |
|
|---|
| 476 | /**
|
|---|
| 477 | * @brief Fast Q15 convolution function (non-sqaure shape)
|
|---|
| 478 | * @param[in] Im_in pointer to input tensor
|
|---|
| 479 | * @param[in] dim_im_in_x input tensor dimention x
|
|---|
| 480 | * @param[in] dim_im_in_y input tensor dimention y
|
|---|
| 481 | * @param[in] ch_im_in number of input tensor channels
|
|---|
| 482 | * @param[in] wt pointer to kernel weights
|
|---|
| 483 | * @param[in] ch_im_out number of filters, i.e., output tensor channels
|
|---|
| 484 | * @param[in] dim_kernel_x filter kernel size x
|
|---|
| 485 | * @param[in] dim_kernel_y filter kernel size y
|
|---|
| 486 | * @param[in] padding_x padding size x
|
|---|
| 487 | * @param[in] padding_y padding size y
|
|---|
| 488 | * @param[in] stride_x convolution stride x
|
|---|
| 489 | * @param[in] stride_y convolution stride y
|
|---|
| 490 | * @param[in] bias pointer to bias
|
|---|
| 491 | * @param[in] bias_shift amount of left-shift for bias
|
|---|
| 492 | * @param[in] out_shift amount of right-shift for output
|
|---|
| 493 | * @param[in,out] Im_out pointer to output tensor
|
|---|
| 494 | * @param[in] dim_im_out_x output tensor dimension x
|
|---|
| 495 | * @param[in] dim_im_out_y output tensor dimension y
|
|---|
| 496 | * @param[in,out] bufferA pointer to buffer space for input
|
|---|
| 497 | * @param[in,out] bufferB pointer to buffer space for output
|
|---|
| 498 | * @return The function returns either
|
|---|
| 499 | * <code>ARM_MATH_SIZE_MISMATCH</code> or <code>ARM_MATH_SUCCESS</code> based on the outcome of size checking.
|
|---|
| 500 | *
|
|---|
| 501 | * @details
|
|---|
| 502 | *
|
|---|
| 503 | * <b>Buffer size:</b>
|
|---|
| 504 | *
|
|---|
| 505 | * bufferA size: 2*ch_im_in*dim_kernel*dim_kernel
|
|---|
| 506 | *
|
|---|
| 507 | * bufferB size: 0
|
|---|
| 508 | *
|
|---|
| 509 | * <b>Input dimension constraints:</b>
|
|---|
| 510 | *
|
|---|
| 511 | * ch_im_in is multiple of 2
|
|---|
| 512 | *
|
|---|
| 513 | * ch_im_out is multipe of 2
|
|---|
| 514 | *
|
|---|
| 515 | */
|
|---|
| 516 |
|
|---|
| 517 | arm_status
|
|---|
| 518 | arm_convolve_HWC_q15_fast_nonsquare(const q15_t * Im_in,
|
|---|
| 519 | const uint16_t dim_im_in_x,
|
|---|
| 520 | const uint16_t dim_im_in_y,
|
|---|
| 521 | const uint16_t ch_im_in,
|
|---|
| 522 | const q15_t * wt,
|
|---|
| 523 | const uint16_t ch_im_out,
|
|---|
| 524 | const uint16_t dim_kernel_x,
|
|---|
| 525 | const uint16_t dim_kernel_y,
|
|---|
| 526 | const uint16_t padding_x,
|
|---|
| 527 | const uint16_t padding_y,
|
|---|
| 528 | const uint16_t stride_x,
|
|---|
| 529 | const uint16_t stride_y,
|
|---|
| 530 | const q15_t * bias,
|
|---|
| 531 | const uint16_t bias_shift,
|
|---|
| 532 | const uint16_t out_shift,
|
|---|
| 533 | q15_t * Im_out,
|
|---|
| 534 | const uint16_t dim_im_out_x,
|
|---|
| 535 | const uint16_t dim_im_out_y,
|
|---|
| 536 | q15_t * bufferA,
|
|---|
| 537 | q7_t * bufferB);
|
|---|
| 538 |
|
|---|
| 539 | /**
|
|---|
| 540 | * @brief Q7 depthwise separable convolution function
|
|---|
| 541 | * @param[in] Im_in pointer to input tensor
|
|---|
| 542 | * @param[in] dim_im_in input tensor dimention
|
|---|
| 543 | * @param[in] ch_im_in number of input tensor channels
|
|---|
| 544 | * @param[in] wt pointer to kernel weights
|
|---|
| 545 | * @param[in] ch_im_out number of filters, i.e., output tensor channels
|
|---|
| 546 | * @param[in] dim_kernel filter kernel size
|
|---|
| 547 | * @param[in] padding padding sizes
|
|---|
| 548 | * @param[in] stride convolution stride
|
|---|
| 549 | * @param[in] bias pointer to bias
|
|---|
| 550 | * @param[in] bias_shift amount of left-shift for bias
|
|---|
| 551 | * @param[in] out_shift amount of right-shift for output
|
|---|
| 552 | * @param[in,out] Im_out pointer to output tensor
|
|---|
| 553 | * @param[in] dim_im_out output tensor dimension
|
|---|
| 554 | * @param[in,out] bufferA pointer to buffer space for input
|
|---|
| 555 | * @param[in,out] bufferB pointer to buffer space for output
|
|---|
| 556 | * @return The function returns either
|
|---|
| 557 | * <code>ARM_MATH_SIZE_MISMATCH</code> or <code>ARM_MATH_SUCCESS</code> based on the outcome of size checking.
|
|---|
| 558 | *
|
|---|
| 559 | * This function is the version with full list of optimization tricks, but with
|
|---|
| 560 | * some contraints:
|
|---|
| 561 | * ch_im_in is multiple of 2
|
|---|
| 562 | * ch_im_out is multiple of 2
|
|---|
| 563 | */
|
|---|
| 564 |
|
|---|
| 565 | arm_status arm_depthwise_separable_conv_HWC_q7(const q7_t * Im_in,
|
|---|
| 566 | const uint16_t dim_im_in,
|
|---|
| 567 | const uint16_t ch_im_in,
|
|---|
| 568 | const q7_t * wt,
|
|---|
| 569 | const uint16_t ch_im_out,
|
|---|
| 570 | const uint16_t dim_kernel,
|
|---|
| 571 | const uint16_t padding,
|
|---|
| 572 | const uint16_t stride,
|
|---|
| 573 | const q7_t * bias,
|
|---|
| 574 | const uint16_t bias_shift,
|
|---|
| 575 | const uint16_t out_shift,
|
|---|
| 576 | q7_t * Im_out,
|
|---|
| 577 | const uint16_t dim_im_out,
|
|---|
| 578 | q15_t * bufferA,
|
|---|
| 579 | q7_t * bufferB);
|
|---|
| 580 |
|
|---|
| 581 | /**
|
|---|
| 582 | * @brief Q7 depthwise separable convolution function (non-square shape)
|
|---|
| 583 | * @param[in] Im_in pointer to input tensor
|
|---|
| 584 | * @param[in] dim_im_in_x input tensor dimention x
|
|---|
| 585 | * @param[in] dim_im_in_y input tensor dimention y
|
|---|
| 586 | * @param[in] ch_im_in number of input tensor channels
|
|---|
| 587 | * @param[in] wt pointer to kernel weights
|
|---|
| 588 | * @param[in] ch_im_out number of filters, i.e., output tensor channels
|
|---|
| 589 | * @param[in] dim_kernel_x filter kernel size x
|
|---|
| 590 | * @param[in] dim_kernel_y filter kernel size y
|
|---|
| 591 | * @param[in] padding_x padding sizes x
|
|---|
| 592 | * @param[in] padding_y padding sizes y
|
|---|
| 593 | * @param[in] stride_x convolution stride x
|
|---|
| 594 | * @param[in] stride_y convolution stride y
|
|---|
| 595 | * @param[in] bias pointer to bias
|
|---|
| 596 | * @param[in] bias_shift amount of left-shift for bias
|
|---|
| 597 | * @param[in] out_shift amount of right-shift for output
|
|---|
| 598 | * @param[in,out] Im_out pointer to output tensor
|
|---|
| 599 | * @param[in] dim_im_out_x output tensor dimension x
|
|---|
| 600 | * @param[in] dim_im_out_y output tensor dimension y
|
|---|
| 601 | * @param[in,out] bufferA pointer to buffer space for input
|
|---|
| 602 | * @param[in,out] bufferB pointer to buffer space for output
|
|---|
| 603 | * @return The function returns either
|
|---|
| 604 | * <code>ARM_MATH_SIZE_MISMATCH</code> or <code>ARM_MATH_SUCCESS</code> based on the outcome of size checking.
|
|---|
| 605 | *
|
|---|
| 606 | * This function is the version with full list of optimization tricks, but with
|
|---|
| 607 | * some contraints:
|
|---|
| 608 | * ch_im_in is multiple of 2
|
|---|
| 609 | * ch_im_out is multiple of 2
|
|---|
| 610 | */
|
|---|
| 611 | arm_status arm_depthwise_separable_conv_HWC_q7_nonsquare(const q7_t * Im_in,
|
|---|
| 612 | const uint16_t dim_im_in_x,
|
|---|
| 613 | const uint16_t dim_im_in_y,
|
|---|
| 614 | const uint16_t ch_im_in,
|
|---|
| 615 | const q7_t * wt,
|
|---|
| 616 | const uint16_t ch_im_out,
|
|---|
| 617 | const uint16_t dim_kernel_x,
|
|---|
| 618 | const uint16_t dim_kernel_y,
|
|---|
| 619 | const uint16_t padding_x,
|
|---|
| 620 | const uint16_t padding_y,
|
|---|
| 621 | const uint16_t stride_x,
|
|---|
| 622 | const uint16_t stride_y,
|
|---|
| 623 | const q7_t * bias,
|
|---|
| 624 | const uint16_t bias_shift,
|
|---|
| 625 | const uint16_t out_shift,
|
|---|
| 626 | q7_t * Im_out,
|
|---|
| 627 | const uint16_t dim_im_out_x,
|
|---|
| 628 | const uint16_t dim_im_out_y,
|
|---|
| 629 | q15_t * bufferA,
|
|---|
| 630 | q7_t * bufferB);
|
|---|
| 631 |
|
|---|
| 632 |
|
|---|
| 633 | /**
|
|---|
| 634 | * @defgroup FC Fully-connected Layer Functions
|
|---|
| 635 | *
|
|---|
| 636 | * Perform fully-connected layer
|
|---|
| 637 | *
|
|---|
| 638 | * Fully-connected layer is basically a matrix-vector multiplication
|
|---|
| 639 | * with bias. The matrix is the weights and the input/output vectors
|
|---|
| 640 | * are the activation values. Supported {weight, activation} precisions
|
|---|
| 641 | * include {8-bit, 8-bit}, {16-bit, 16-bit}, and {8-bit, 16-bit}.
|
|---|
| 642 | *
|
|---|
| 643 | * Here we have two types of kernel functions. The basic function
|
|---|
| 644 | * implements the function using regular GEMV approach. The opt functions
|
|---|
| 645 | * operates with weights in interleaved formats.
|
|---|
| 646 | *
|
|---|
| 647 | */
|
|---|
| 648 |
|
|---|
| 649 | /**
|
|---|
| 650 | * @brief Q7 basic fully-connected layer function
|
|---|
| 651 | * @param[in] pV pointer to input vector
|
|---|
| 652 | * @param[in] pM pointer to matrix weights
|
|---|
| 653 | * @param[in] dim_vec length of the vector
|
|---|
| 654 | * @param[in] num_of_rows number of rows in weight matrix
|
|---|
| 655 | * @param[in] bias_shift amount of left-shift for bias
|
|---|
| 656 | * @param[in] out_shift amount of right-shift for output
|
|---|
| 657 | * @param[in] bias pointer to bias
|
|---|
| 658 | * @param[in,out] pOut pointer to output vector
|
|---|
| 659 | * @param[in,out] vec_buffer pointer to buffer space for input
|
|---|
| 660 | * @return The function returns <code>ARM_MATH_SUCCESS</code>
|
|---|
| 661 | *
|
|---|
| 662 | */
|
|---|
| 663 |
|
|---|
| 664 | arm_status arm_fully_connected_q7(const q7_t * pV,
|
|---|
| 665 | const q7_t * pM,
|
|---|
| 666 | const uint16_t dim_vec,
|
|---|
| 667 | const uint16_t num_of_rows,
|
|---|
| 668 | const uint16_t bias_shift,
|
|---|
| 669 | const uint16_t out_shift,
|
|---|
| 670 | const q7_t * bias,
|
|---|
| 671 | q7_t * pOut,
|
|---|
| 672 | q15_t * vec_buffer);
|
|---|
| 673 |
|
|---|
| 674 | /**
|
|---|
| 675 | * @brief Q7 opt fully-connected layer function
|
|---|
| 676 | * @param[in] pV pointer to input vector
|
|---|
| 677 | * @param[in] pM pointer to matrix weights
|
|---|
| 678 | * @param[in] dim_vec length of the vector
|
|---|
| 679 | * @param[in] num_of_rows number of rows in weight matrix
|
|---|
| 680 | * @param[in] bias_shift amount of left-shift for bias
|
|---|
| 681 | * @param[in] out_shift amount of right-shift for output
|
|---|
| 682 | * @param[in] bias pointer to bias
|
|---|
| 683 | * @param[in,out] pOut pointer to output vector
|
|---|
| 684 | * @param[in,out] vec_buffer pointer to buffer space for input
|
|---|
| 685 | * @return The function returns <code>ARM_MATH_SUCCESS</code>
|
|---|
| 686 | *
|
|---|
| 687 | */
|
|---|
| 688 |
|
|---|
| 689 | arm_status arm_fully_connected_q7_opt(const q7_t * pV,
|
|---|
| 690 | const q7_t * pM,
|
|---|
| 691 | const uint16_t dim_vec,
|
|---|
| 692 | const uint16_t num_of_rows,
|
|---|
| 693 | const uint16_t bias_shift,
|
|---|
| 694 | const uint16_t out_shift,
|
|---|
| 695 | const q7_t * bias,
|
|---|
| 696 | q7_t * pOut,
|
|---|
| 697 | q15_t * vec_buffer);
|
|---|
| 698 |
|
|---|
| 699 | /**
|
|---|
| 700 | * @brief Q15 basic fully-connected layer function
|
|---|
| 701 | * @param[in] pV pointer to input vector
|
|---|
| 702 | * @param[in] pM pointer to matrix weights
|
|---|
| 703 | * @param[in] dim_vec length of the vector
|
|---|
| 704 | * @param[in] num_of_rows number of rows in weight matrix
|
|---|
| 705 | * @param[in] bias_shift amount of left-shift for bias
|
|---|
| 706 | * @param[in] out_shift amount of right-shift for output
|
|---|
| 707 | * @param[in] bias pointer to bias
|
|---|
| 708 | * @param[in,out] pOut pointer to output vector
|
|---|
| 709 | * @param[in,out] vec_buffer pointer to buffer space for input
|
|---|
| 710 | * @return The function returns <code>ARM_MATH_SUCCESS</code>
|
|---|
| 711 | *
|
|---|
| 712 | */
|
|---|
| 713 |
|
|---|
| 714 | arm_status arm_fully_connected_q15(const q15_t * pV,
|
|---|
| 715 | const q15_t * pM,
|
|---|
| 716 | const uint16_t dim_vec,
|
|---|
| 717 | const uint16_t num_of_rows,
|
|---|
| 718 | const uint16_t bias_shift,
|
|---|
| 719 | const uint16_t out_shift,
|
|---|
| 720 | const q15_t * bias,
|
|---|
| 721 | q15_t * pOut,
|
|---|
| 722 | q15_t * vec_buffer);
|
|---|
| 723 |
|
|---|
| 724 | /**
|
|---|
| 725 | * @brief Q15 opt fully-connected layer function
|
|---|
| 726 | * @param[in] pV pointer to input vector
|
|---|
| 727 | * @param[in] pM pointer to matrix weights
|
|---|
| 728 | * @param[in] dim_vec length of the vector
|
|---|
| 729 | * @param[in] num_of_rows number of rows in weight matrix
|
|---|
| 730 | * @param[in] bias_shift amount of left-shift for bias
|
|---|
| 731 | * @param[in] out_shift amount of right-shift for output
|
|---|
| 732 | * @param[in] bias pointer to bias
|
|---|
| 733 | * @param[in,out] pOut pointer to output vector
|
|---|
| 734 | * @param[in,out] vec_buffer pointer to buffer space for input
|
|---|
| 735 | * @return The function returns <code>ARM_MATH_SUCCESS</code>
|
|---|
| 736 | *
|
|---|
| 737 | */
|
|---|
| 738 |
|
|---|
| 739 | arm_status arm_fully_connected_q15_opt(const q15_t * pV,
|
|---|
| 740 | const q15_t * pM,
|
|---|
| 741 | const uint16_t dim_vec,
|
|---|
| 742 | const uint16_t num_of_rows,
|
|---|
| 743 | const uint16_t bias_shift,
|
|---|
| 744 | const uint16_t out_shift,
|
|---|
| 745 | const q15_t * bias,
|
|---|
| 746 | q15_t * pOut,
|
|---|
| 747 | q15_t * vec_buffer);
|
|---|
| 748 |
|
|---|
| 749 | /**
|
|---|
| 750 | * @brief Mixed Q15-Q7 fully-connected layer function
|
|---|
| 751 | * @param[in] pV pointer to input vector
|
|---|
| 752 | * @param[in] pM pointer to matrix weights
|
|---|
| 753 | * @param[in] dim_vec length of the vector
|
|---|
| 754 | * @param[in] num_of_rows number of rows in weight matrix
|
|---|
| 755 | * @param[in] bias_shift amount of left-shift for bias
|
|---|
| 756 | * @param[in] out_shift amount of right-shift for output
|
|---|
| 757 | * @param[in] bias pointer to bias
|
|---|
| 758 | * @param[in,out] pOut pointer to output vector
|
|---|
| 759 | * @param[in,out] vec_buffer pointer to buffer space for input
|
|---|
| 760 | * @return The function returns <code>ARM_MATH_SUCCESS</code>
|
|---|
| 761 | *
|
|---|
| 762 | */
|
|---|
| 763 |
|
|---|
| 764 | arm_status arm_fully_connected_mat_q7_vec_q15(const q15_t * pV,
|
|---|
| 765 | const q7_t * pM,
|
|---|
| 766 | const uint16_t dim_vec,
|
|---|
| 767 | const uint16_t num_of_rows,
|
|---|
| 768 | const uint16_t bias_shift,
|
|---|
| 769 | const uint16_t out_shift,
|
|---|
| 770 | const q7_t * bias,
|
|---|
| 771 | q15_t * pOut,
|
|---|
| 772 | q15_t * vec_buffer);
|
|---|
| 773 |
|
|---|
| 774 | /**
|
|---|
| 775 | * @brief Mixed Q15-Q7 opt fully-connected layer function
|
|---|
| 776 | * @param[in] pV pointer to input vector
|
|---|
| 777 | * @param[in] pM pointer to matrix weights
|
|---|
| 778 | * @param[in] dim_vec length of the vector
|
|---|
| 779 | * @param[in] num_of_rows number of rows in weight matrix
|
|---|
| 780 | * @param[in] bias_shift amount of left-shift for bias
|
|---|
| 781 | * @param[in] out_shift amount of right-shift for output
|
|---|
| 782 | * @param[in] bias pointer to bias
|
|---|
| 783 | * @param[in,out] pOut pointer to output vector
|
|---|
| 784 | * @param[in,out] vec_buffer pointer to buffer space for input
|
|---|
| 785 | * @return The function returns <code>ARM_MATH_SUCCESS</code>
|
|---|
| 786 | *
|
|---|
| 787 | */
|
|---|
| 788 |
|
|---|
| 789 | arm_status arm_fully_connected_mat_q7_vec_q15_opt(const q15_t * pV,
|
|---|
| 790 | const q7_t * pM,
|
|---|
| 791 | const uint16_t dim_vec,
|
|---|
| 792 | const uint16_t num_of_rows,
|
|---|
| 793 | const uint16_t bias_shift,
|
|---|
| 794 | const uint16_t out_shift,
|
|---|
| 795 | const q7_t * bias,
|
|---|
| 796 | q15_t * pOut,
|
|---|
| 797 | q15_t * vec_buffer);
|
|---|
| 798 |
|
|---|
| 799 | /**
|
|---|
| 800 | * @brief Matrix-Multiplication Kernels for Convolution
|
|---|
| 801 | *
|
|---|
| 802 | * These functions are used within convolution layer functions for
|
|---|
| 803 | * matrix multiplication.
|
|---|
| 804 | *
|
|---|
| 805 | * The implementation is similar to CMSIS-DSP arm_mat_mult functions
|
|---|
| 806 | * with one Q7 and one Q15 operands. The Q15 operand is the im2col
|
|---|
| 807 | * output which is always with 2 columns.
|
|---|
| 808 | *
|
|---|
| 809 | */
|
|---|
| 810 |
|
|---|
| 811 | /**
|
|---|
| 812 | * @brief Matrix-multiplication function for convolution
|
|---|
| 813 | * @param[in] pA pointer to operand A
|
|---|
| 814 | * @param[in] pInBuffer pointer to operand B, always conssists of 2 vectors
|
|---|
| 815 | * @param[in] ch_im_out numRow of A
|
|---|
| 816 | * @param[in] numCol_A numCol of A
|
|---|
| 817 | * @param[in] bias_shift amount of left-shift for bias
|
|---|
| 818 | * @param[in] out_shift amount of right-shift for output
|
|---|
| 819 | * @param[in] bias the bias
|
|---|
| 820 | * @param[in,out] pOut pointer to output
|
|---|
| 821 | * @return The function returns the incremented output pointer
|
|---|
| 822 | */
|
|---|
| 823 |
|
|---|
| 824 | q7_t *arm_nn_mat_mult_kernel_q7_q15(const q7_t * pA,
|
|---|
| 825 | const q15_t * pInBuffer,
|
|---|
| 826 | const uint16_t ch_im_out,
|
|---|
| 827 | const uint16_t numCol_A,
|
|---|
| 828 | const uint16_t bias_shift,
|
|---|
| 829 | const uint16_t out_shift,
|
|---|
| 830 | const q7_t * bias,
|
|---|
| 831 | q7_t * pOut);
|
|---|
| 832 |
|
|---|
| 833 | /**
|
|---|
| 834 | * @brief Matrix-multiplication function for convolution with reordered columns
|
|---|
| 835 | * @param[in] pA pointer to operand A
|
|---|
| 836 | * @param[in] pInBuffer pointer to operand B, always conssists of 2 vectors
|
|---|
| 837 | * @param[in] ch_im_out numRow of A
|
|---|
| 838 | * @param[in] numCol_A numCol of A
|
|---|
| 839 | * @param[in] bias_shift amount of left-shift for bias
|
|---|
| 840 | * @param[in] out_shift amount of right-shift for output
|
|---|
| 841 | * @param[in] bias the bias
|
|---|
| 842 | * @param[in,out] pOut pointer to output
|
|---|
| 843 | * @return The function returns the incremented output pointer
|
|---|
| 844 | */
|
|---|
| 845 |
|
|---|
| 846 | q7_t *arm_nn_mat_mult_kernel_q7_q15_reordered(const q7_t * pA,
|
|---|
| 847 | const q15_t * pInBuffer,
|
|---|
| 848 | const uint16_t ch_im_out,
|
|---|
| 849 | const uint16_t numCol_A,
|
|---|
| 850 | const uint16_t bias_shift,
|
|---|
| 851 | const uint16_t out_shift,
|
|---|
| 852 | const q7_t * bias,
|
|---|
| 853 | q7_t * pOut);
|
|---|
| 854 |
|
|---|
| 855 | #ifdef __cplusplus
|
|---|
| 856 | }
|
|---|
| 857 | #endif
|
|---|
| 858 |
|
|---|
| 859 | /*
|
|---|
| 860 | * Other functions
|
|---|
| 861 | * These layers are typically not timing critical
|
|---|
| 862 | * Basic implementation is supported here
|
|---|
| 863 | */
|
|---|
| 864 |
|
|---|
| 865 | #ifdef __cplusplus
|
|---|
| 866 | extern "C"
|
|---|
| 867 | {
|
|---|
| 868 | #endif
|
|---|
| 869 |
|
|---|
| 870 | /**
|
|---|
| 871 | * @defgroup Acti Neural Network Activation Functions
|
|---|
| 872 | *
|
|---|
| 873 | * Perform activation layers, including ReLU (Rectified Linear Unit),
|
|---|
| 874 | * sigmoid and tanh
|
|---|
| 875 | *
|
|---|
| 876 | */
|
|---|
| 877 |
|
|---|
| 878 | /**
|
|---|
| 879 | * @brief Q7 RELU function
|
|---|
| 880 | * @param[in,out] data pointer to input
|
|---|
| 881 | * @param[in] size number of elements
|
|---|
| 882 | * @return none.
|
|---|
| 883 | */
|
|---|
| 884 |
|
|---|
| 885 | void arm_relu_q7(q7_t * data, uint16_t size);
|
|---|
| 886 |
|
|---|
| 887 | /**
|
|---|
| 888 | * @brief Q15 RELU function
|
|---|
| 889 | * @param[in,out] data pointer to input
|
|---|
| 890 | * @param[in] size number of elements
|
|---|
| 891 | * @return none.
|
|---|
| 892 | */
|
|---|
| 893 |
|
|---|
| 894 | void arm_relu_q15(q15_t * data, uint16_t size);
|
|---|
| 895 |
|
|---|
| 896 | /**
|
|---|
| 897 | * @brief Q7 neural network activation function using direct table look-up
|
|---|
| 898 | * @param[in,out] data pointer to input
|
|---|
| 899 | * @param[in] size number of elements
|
|---|
| 900 | * @param[in] int_width bit-width of the integer part, assume to be smaller than 3
|
|---|
| 901 | * @param[in] type type of activation functions
|
|---|
| 902 | * @return none.
|
|---|
| 903 | */
|
|---|
| 904 |
|
|---|
| 905 | void arm_nn_activations_direct_q7(q7_t * data, uint16_t size, uint16_t int_width,
|
|---|
| 906 | arm_nn_activation_type type);
|
|---|
| 907 |
|
|---|
| 908 | /**
|
|---|
| 909 | * @brief Q15 neural network activation function using direct table look-up
|
|---|
| 910 | * @param[in,out] data pointer to input
|
|---|
| 911 | * @param[in] size number of elements
|
|---|
| 912 | * @param[in] int_width bit-width of the integer part, assume to be smaller than 3
|
|---|
| 913 | * @param[in] type type of activation functions
|
|---|
| 914 | * @return none.
|
|---|
| 915 | */
|
|---|
| 916 |
|
|---|
| 917 | void arm_nn_activations_direct_q15(q15_t * data, uint16_t size, uint16_t int_width,
|
|---|
| 918 | arm_nn_activation_type type);
|
|---|
| 919 |
|
|---|
| 920 | /**
|
|---|
| 921 | * @defgroup Pooling Neural Network Pooling Functions
|
|---|
| 922 | *
|
|---|
| 923 | * Perform pooling functions, including max pooling and average pooling
|
|---|
| 924 | *
|
|---|
| 925 | */
|
|---|
| 926 |
|
|---|
| 927 | /**
|
|---|
| 928 | * @brief Q7 max pooling function
|
|---|
| 929 | * @param[in] Im_in pointer to input tensor
|
|---|
| 930 | * @param[in] dim_im_in input tensor dimention
|
|---|
| 931 | * @param[in] ch_im_in number of input tensor channels
|
|---|
| 932 | * @param[in] dim_kernel filter kernel size
|
|---|
| 933 | * @param[in] padding padding sizes
|
|---|
| 934 | * @param[in] stride convolution stride
|
|---|
| 935 | * @param[in] dim_im_out output tensor dimension
|
|---|
| 936 | * @param[in,out] bufferA pointer to buffer space for input
|
|---|
| 937 | * @param[in,out] Im_out pointer to output tensor
|
|---|
| 938 | * @return none.
|
|---|
| 939 | *
|
|---|
| 940 | */
|
|---|
| 941 |
|
|---|
| 942 | void arm_maxpool_q7_HWC(q7_t * Im_in,
|
|---|
| 943 | const uint16_t dim_im_in,
|
|---|
| 944 | const uint16_t ch_im_in,
|
|---|
| 945 | const uint16_t dim_kernel,
|
|---|
| 946 | const uint16_t padding,
|
|---|
| 947 | const uint16_t stride,
|
|---|
| 948 | const uint16_t dim_im_out,
|
|---|
| 949 | q7_t * bufferA,
|
|---|
| 950 | q7_t * Im_out);
|
|---|
| 951 |
|
|---|
| 952 | /**
|
|---|
| 953 | * @brief Q7 average pooling function
|
|---|
| 954 | * @param[in] Im_in pointer to input tensor
|
|---|
| 955 | * @param[in] dim_im_in input tensor dimention
|
|---|
| 956 | * @param[in] ch_im_in number of input tensor channels
|
|---|
| 957 | * @param[in] dim_kernel filter kernel size
|
|---|
| 958 | * @param[in] padding padding sizes
|
|---|
| 959 | * @param[in] stride convolution stride
|
|---|
| 960 | * @param[in] dim_im_out output tensor dimension
|
|---|
| 961 | * @param[in,out] bufferA pointer to buffer space for input
|
|---|
| 962 | * @param[in,out] Im_out pointer to output tensor
|
|---|
| 963 | * @return none.
|
|---|
| 964 | *
|
|---|
| 965 | */
|
|---|
| 966 |
|
|---|
| 967 | void arm_avepool_q7_HWC(q7_t * Im_in,
|
|---|
| 968 | const uint16_t dim_im_in,
|
|---|
| 969 | const uint16_t ch_im_in,
|
|---|
| 970 | const uint16_t dim_kernel,
|
|---|
| 971 | const uint16_t padding,
|
|---|
| 972 | const uint16_t stride,
|
|---|
| 973 | const uint16_t dim_im_out,
|
|---|
| 974 | q7_t * bufferA,
|
|---|
| 975 | q7_t * Im_out);
|
|---|
| 976 |
|
|---|
| 977 | /**
|
|---|
| 978 | * @defgroup Softmax Softmax Functions
|
|---|
| 979 | *
|
|---|
| 980 | * EXP(2) based softmax function
|
|---|
| 981 | *
|
|---|
| 982 | */
|
|---|
| 983 |
|
|---|
| 984 | /**
|
|---|
| 985 | * @brief Q7 softmax function
|
|---|
| 986 | * @param[in] vec_in pointer to input vector
|
|---|
| 987 | * @param[in] dim_vec input vector dimention
|
|---|
| 988 | * @param[out] p_out pointer to output vector
|
|---|
| 989 | * @return none.
|
|---|
| 990 | *
|
|---|
| 991 | */
|
|---|
| 992 |
|
|---|
| 993 | void arm_softmax_q7(const q7_t * vec_in, const uint16_t dim_vec, q7_t * p_out);
|
|---|
| 994 |
|
|---|
| 995 | /**
|
|---|
| 996 | * @brief Q15 softmax function
|
|---|
| 997 | * @param[in] vec_in pointer to input vector
|
|---|
| 998 | * @param[in] dim_vec input vector dimention
|
|---|
| 999 | * @param[out] p_out pointer to output vector
|
|---|
| 1000 | * @return none.
|
|---|
| 1001 | *
|
|---|
| 1002 | */
|
|---|
| 1003 |
|
|---|
| 1004 | void arm_softmax_q15(const q15_t * vec_in, const uint16_t dim_vec, q15_t * p_out);
|
|---|
| 1005 |
|
|---|
| 1006 | #ifdef __cplusplus
|
|---|
| 1007 | }
|
|---|
| 1008 | #endif
|
|---|
| 1009 |
|
|---|
| 1010 | #endif
|
|---|
