1187 lines
49 KiB
C
1187 lines
49 KiB
C
/*
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* Copyright (C) 2010-2022 Arm Limited or its affiliates.
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*
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* SPDX-License-Identifier: Apache-2.0
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*
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* Licensed under the Apache License, Version 2.0 (the License); you may
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* not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an AS IS BASIS, WITHOUT
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* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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/* ----------------------------------------------------------------------
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* Project: CMSIS NN Library
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* Title: arm_nnsupportfunctions.h
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* Description: Public header file of support functions for CMSIS NN Library
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*
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* $Date: 19. April 2022
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* $Revision: V.7.0.1
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*
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* Target Processor: Cortex-M CPUs
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* -------------------------------------------------------------------- */
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#ifndef _ARM_NNSUPPORTFUNCTIONS_H_
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#define _ARM_NNSUPPORTFUNCTIONS_H_
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#include "arm_nn_math_types.h"
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#include "arm_nn_types.h"
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#include <stdbool.h>
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#ifdef __cplusplus
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extern "C" {
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#endif
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#define LEFT_SHIFT(_shift) (_shift > 0 ? _shift : 0)
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#define RIGHT_SHIFT(_shift) (_shift > 0 ? 0 : -_shift)
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#define MASK_IF_ZERO(x) (x) == 0 ? ~0 : 0
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#define MASK_IF_NON_ZERO(x) (x) != 0 ? ~0 : 0
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#define SELECT_USING_MASK(mask, a, b) ((mask) & (a)) ^ (~(mask) & (b))
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#define MAX(A, B) ((A) > (B) ? (A) : (B))
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#define MIN(A, B) ((A) < (B) ? (A) : (B))
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#define CLAMP(x, h, l) MAX(MIN((x), (h)), (l))
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#define REDUCE_MULTIPLIER(_mult) ((_mult < 0x7FFF0000) ? ((_mult + (1 << 15)) >> 16) : 0x7FFF)
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/**
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* @brief definition to pack four 8 bit values.
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*/
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#define PACK_Q7x4_32x1(v0, v1, v2, v3) \
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((((int32_t)(v0) << 0) & (int32_t)0x000000FF) | (((int32_t)(v1) << 8) & (int32_t)0x0000FF00) | \
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(((int32_t)(v2) << 16) & (int32_t)0x00FF0000) | (((int32_t)(v3) << 24) & (int32_t)0xFF000000))
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/**
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* @brief Union for SIMD access of q31/q15/q7 types
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*/
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union arm_nnword
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{
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q31_t word;
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/**< q31 type */
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q15_t half_words[2];
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/**< q15 type */
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q7_t bytes[4];
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/**< q7 type */
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};
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/**
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* @brief Union for data type long long
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*/
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struct arm_nn_double
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{
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uint32_t low;
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int32_t high;
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};
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union arm_nn_long_long
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{
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int64_t long_long;
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struct arm_nn_double word;
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};
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/**
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* @defgroup nndata_convert Neural Network Data Conversion Functions
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*
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* Perform data type conversion in-between neural network operations
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*
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*/
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/**
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* @brief Converts the elements of the q7 vector to q15 vector without left-shift
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* @param[in] *pSrc points to the q7 input vector
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* @param[out] *pDst points to the q15 output vector
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* @param[in] blockSize length of the input vector
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*
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*/
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void arm_q7_to_q15_no_shift(const q7_t *pSrc, q15_t *pDst, uint32_t blockSize);
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/**
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* @brief Non-saturating addition of elements of a q7 vector
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* @param[in] *input Pointer to the q7 input vector
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* @param[out] *output Pointer to the q31 output variable.
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* @param[in] block_size length of the input vector
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* \par Description:
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*
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* 2^24 samples can be added without saturating the result.
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*
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* The equation used for the conversion process is:
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*
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* <pre>
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* sum = input[0] + input[1] + .. + input[block_size -1]
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* </pre>
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*
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* */
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void arm_nn_add_q7(const q7_t *input, q31_t *output, uint32_t block_size);
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/**
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* @brief Converts the elements of the q7 vector to reordered q15 vector without left-shift
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* @param[in] *pSrc points to the q7 input vector
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* @param[out] *pDst points to the q15 output vector
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* @param[in] blockSize length of the input vector
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* @return none.
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*
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*/
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void arm_q7_to_q15_reordered_no_shift(const q7_t *pSrc, q15_t *pDst, uint32_t blockSize);
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/**
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* @brief Converts the elements from a q7 vector to a q15 vector with an added offset
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* @param[in] src pointer to the q7 input vector
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* @param[out] dst pointer to the q15 output vector
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* @param[in] block_size length of the input vector
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* @param[in] offset q7 offset to be added to each input vector element.
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*
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* \par Description:
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*
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* The equation used for the conversion process is:
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*
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* <pre>
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* dst[n] = (q15_t) src[n] + offset; 0 <= n < block_size.
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* </pre>
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*
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*/
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void arm_q7_to_q15_with_offset(const q7_t *src, q15_t *dst, uint32_t block_size, q15_t offset);
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/**
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* @brief Converts the elements of the q7 vector to reordered q15 vector with an added offset
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* @param[in] src pointer to the q7 input vector
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* @param[out] dst pointer to the q15 output vector
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* @param[in] block_size length of the input vector
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* @param[in] offset offset to be added to each input vector element.
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* @return none.
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*
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* @details This function does the q7 to q15 expansion with re-ordering of bytes. Re-ordering is a consequence of
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* the sign extension intrinsic(DSP extension). The tail (i.e., last (N % 4) elements) retains its
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* original order.
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*
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*/
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void arm_q7_to_q15_reordered_with_offset(const q7_t *src, q15_t *dst, uint32_t block_size, q15_t offset);
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/**
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* @brief Converts the elements from a q7 vector and accumulate to a q15 vector
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* @param[in] *src points to the q7 input vector
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* @param[out] *dst points to the q15 output vector
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* @param[in] block_size length of the input vector
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*
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* \par Description:
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*
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* The equation used for the conversion process is:
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*
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* <pre>
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* dst[n] += (q15_t) src[n] ; 0 <= n < block_size.
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* </pre>
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*
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*/
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void arm_nn_accumulate_q7_to_q15(q15_t *dst, const q7_t *src, uint32_t block_size);
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/**
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* @brief Depthwise conv on an im2col buffer where the input channel equals output channel.
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* @param[in] row pointer to row
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* @param[in] col pointer to im2col buffer, always consists of 2 columns.
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* @param[in] num_ch number of channels
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* @param[in] out_shift pointer to per output channel requantization shift parameter.
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* @param[in] out_mult pointer to per output channel requantization multiplier parameter.
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* @param[in] out_offset output tensor offset.
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* @param[in] activation_min minimum value to clamp the output to. Range : int8
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* @param[in] activation_max maximum value to clamp the output to. Range : int8
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* @param[in] kernel_size number of elements in one column.
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* @param[in] output_bias per output channel bias. Range : int32
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* @param[out] out pointer to output
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* @return The function returns one of the two
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* 1. The incremented output pointer for a successful operation or
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* 2. NULL if implementation is not available.
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*
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* @details Supported framework: TensorFlow Lite micro.
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*/
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q7_t *arm_nn_depthwise_conv_s8_core(const q7_t *row,
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const q15_t *col,
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const uint16_t num_ch,
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const int32_t *out_shift,
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const int32_t *out_mult,
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const int32_t out_offset,
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const int32_t activation_min,
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const int32_t activation_max,
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const uint16_t kernel_size,
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const int32_t *const output_bias,
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q7_t *out);
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/**
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* @brief General Matrix-multiplication function with per-channel requantization.
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* @param[in] input_row pointer to row operand
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* @param[in] input_col pointer to col operand
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* @param[in] output_ch number of rows of input_row
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* @param[in] col_batches number of column batches. Range: 1 to 4
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* @param[in] output_shift pointer to per output channel requantization shift parameter.
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* @param[in] output_mult pointer to per output channel requantization multiplier parameter.
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* @param[in] out_offset output tensor offset.
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* @param[in] col_offset input tensor(col) offset.
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* @param[in] row_offset kernel offset(row). Not used.
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* @param[in] out_activation_min minimum value to clamp the output to. Range : int8
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* @param[in] out_activation_max maximum value to clamp the output to. Range : int8
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* @param[in] row_len number of elements in each row
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* @param[in] bias per output channel bias. Range : int32
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* @param[in,out] out pointer to output
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* @return The function returns one of the two
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* 1. The incremented output pointer for a successful operation or
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* 2. NULL if implementation is not available.
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*
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* @details Supported framework: TensorFlow Lite
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*/
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q7_t *arm_nn_mat_mult_s8(const q7_t *input_row,
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const q7_t *input_col,
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const uint16_t output_ch,
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const uint16_t col_batches,
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const int32_t *output_shift,
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const int32_t *output_mult,
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const int32_t out_offset,
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const int32_t col_offset,
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const int32_t row_offset,
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const int16_t out_activation_min,
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const int16_t out_activation_max,
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const uint16_t row_len,
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const int32_t *const bias,
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q7_t *out);
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/**
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* @brief Matrix-multiplication function for convolution with per-channel requantization for 16 bits convolution.
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* @param[in] input_a pointer to operand A
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* @param[in] input_b pointer to operand B, always consists of 2 vectors.
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* @param[in] output_ch number of rows of A
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* @param[in] out_shift pointer to per output channel requantization shift parameter.
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* @param[in] out_mult pointer to per output channel requantization multiplier parameter.
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* @param[in] activation_min minimum value to clamp the output to. Range : int16
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* @param[in] activation_max maximum value to clamp the output to. Range : int16
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* @param[in] num_col_a number of columns of A
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* @param[in] output_bias per output channel bias. Range : int64
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* @param[in,out] out_0 pointer to output
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* @return The function returns one of the two
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* 1. The incremented output pointer for a successful operation or
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* 2. NULL if implementation is not available.
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*
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* @details This function does the matrix multiplication of weight matrix for all output channels
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* with 2 columns from im2col and produces two elements/output_channel. The outputs are
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* clamped in the range provided by activation min and max.
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* Supported framework: TensorFlow Lite micro.
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*/
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q15_t *arm_nn_mat_mult_kernel_s16(const q7_t *input_a,
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const q15_t *input_b,
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const int32_t output_ch,
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const int32_t *out_shift,
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const int32_t *out_mult,
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const int16_t activation_min,
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const int16_t activation_max,
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const int32_t num_col_a,
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const int64_t *const output_bias,
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q15_t *out_0);
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/**
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* @brief General Matrix-multiplication without requantization for one row & one column
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* @param[in] row_elements number of row elements
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* @param[in] row_base pointer to row operand
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* @param[in] col_base pointer to col operand
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* @param[out] sum_col pointer to store sum of column elements
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* @param[out] output pointer to store result of multiply-accumulate
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* @return The function returns the multiply-accumulated result of the row by column.
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*
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* @details Pseudo-code
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* *output = 0
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* sum_col = 0
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* for (i = 0; i < row_elements; i++)
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* *output += row_base[i] * col_base[i]
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* sum_col += col_base[i]
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*
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*/
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arm_status arm_nn_mat_mul_core_1x_s8(int32_t row_elements,
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const int8_t *row_base,
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const int8_t *col_base,
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int32_t *const sum_col,
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int32_t *const output);
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/**
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* @brief Matrix-multiplication with requantization & activation function for four rows and one column
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* @param[in] row_elements number of row elements
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* @param[in] offset offset between rows. Can be the same as row_elements.
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* For e.g, in a 1x1 conv scenario with stride as 1.
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* @param[in] row_base pointer to row operand
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* @param[in] col_base pointer to col operand
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* @param[in] out_ch Number of output channels
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* @param[in] conv_params Pointer to convolution parameters like offsets and activation values
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* @param[in] quant_params Pointer to per-channel quantization parameters
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* @param[in] bias Pointer to per-channel bias
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* @param[out] output Pointer to output where int8 results are stored.
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*
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* @return The function returns the updated output pointer or NULL if implementation is not available.
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*
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* @details Compliant to TFLM int8 specification. MVE implementation only
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*/
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int8_t *arm_nn_mat_mul_core_4x_s8(const int32_t row_elements,
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const int32_t offset,
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const int8_t *row_base,
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const int8_t *col_base,
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const int32_t out_ch,
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const cmsis_nn_conv_params *conv_params,
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const cmsis_nn_per_channel_quant_params *quant_params,
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const int32_t *bias,
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int8_t *output);
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/**
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* @brief General Matrix-multiplication function with per-channel requantization.
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* This function assumes:
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* - LHS input matrix NOT transposed (nt)
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* - RHS input matrix transposed (t)
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*
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* @note This operation also performs the broadcast bias addition before the requantization
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*
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* @param[in] lhs Pointer to the LHS input matrix
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* @param[in] rhs Pointer to the RHS input matrix
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* @param[in] bias Pointer to the bias vector. The length of this vector is equal to the number of
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* output columns (or RHS input rows)
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* @param[out] dst Pointer to the output matrix with "m" rows and "n" columns
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* @param[in] dst_multipliers Pointer to the multipliers vector needed for the per-channel requantization.
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* The length of this vector is equal to the number of output columns (or RHS input
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* rows)
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* @param[in] dst_shifts Pointer to the shifts vector needed for the per-channel requantization. The length
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* of this vector is equal to the number of output columns (or RHS input rows)
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* @param[in] lhs_rows Number of LHS input rows
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* @param[in] rhs_rows Number of RHS input rows
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* @param[in] rhs_cols Number of LHS/RHS input columns
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* @param[in] lhs_offset Offset to be applied to the LHS input value
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* @param[in] dst_offset Offset to be applied the output result
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* @param[in] activation_min Minimum value to clamp down the output. Range : int8
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* @param[in] activation_max Maximum value to clamp up the output. Range : int8
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*
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* @return The function returns <code>ARM_MATH_SUCCESS</code>
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*
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*/
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arm_status arm_nn_mat_mult_nt_t_s8(const q7_t *lhs,
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const q7_t *rhs,
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const q31_t *bias,
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q7_t *dst,
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const int32_t *dst_multipliers,
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const int32_t *dst_shifts,
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const int32_t lhs_rows,
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const int32_t rhs_rows,
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const int32_t rhs_cols,
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const int32_t lhs_offset,
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const int32_t dst_offset,
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const int32_t activation_min,
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const int32_t activation_max);
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/**
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* @brief s8 Vector by Matrix (transposed) multiplication
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*
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* @param[in] lhs Input left-hand side vector
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* @param[in] rhs Input right-hand side matrix (transposed)
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* @param[in] bias Input bias
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* @param[out] dst Output vector
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* @param[in] lhs_offset Offset to be added to the input values of the left-hand side vector.
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* Range: -127 to 128
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* @param[in] rhs_offset Not used
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* @param[in] dst_offset Offset to be added to the output values. Range: -127 to 128
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* @param[in] dst_multiplier Output multiplier
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* @param[in] dst_shift Output shift
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* @param[in] rhs_cols Number of columns in the right-hand side input matrix
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* @param[in] rhs_rows Number of rows in the right-hand side input matrix
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* @param[in] activation_min Minimum value to clamp the output to. Range: int8
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* @param[in] activation_max Maximum value to clamp the output to. Range: int8
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* @param[in] address_offset Memory position offset for dst. First output is stored at 'dst', the
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* second at 'dst + address_offset' and so on. Default value is typically 1.
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*
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* @return The function returns <code>ARM_MATH_SUCCESS</code>
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*
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*/
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arm_status arm_nn_vec_mat_mult_t_s8(const q7_t *lhs,
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const q7_t *rhs,
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const q31_t *bias,
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q7_t *dst,
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const int32_t lhs_offset,
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const int32_t rhs_offset,
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const int32_t dst_offset,
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const int32_t dst_multiplier,
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const int32_t dst_shift,
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const int32_t rhs_cols,
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const int32_t rhs_rows,
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const int32_t activation_min,
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const int32_t activation_max,
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const int32_t address_offset);
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/**
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* @brief s16 Vector by Matrix (transposed) multiplication
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*
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* @param[in] lhs Input left-hand side vector
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* @param[in] rhs Input right-hand side matrix (transposed)
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* @param[in] bias Input bias
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* @param[out] dst Output vector
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* @param[in] dst_multiplier Output multiplier
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* @param[in] dst_shift Output shift
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* @param[in] rhs_cols Number of columns in the right-hand side input matrix
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* @param[in] rhs_rows Number of rows in the right-hand side input matrix
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* @param[in] activation_min Minimum value to clamp the output to. Range: int16
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* @param[in] activation_max Maximum value to clamp the output to. Range: int16
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*
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* @return The function returns <code>ARM_MATH_SUCCESS</code>
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*
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*/
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arm_status arm_nn_vec_mat_mult_t_s16(const q15_t *lhs,
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const q7_t *rhs,
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const q63_t *bias,
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q15_t *dst,
|
|
const int32_t dst_multiplier,
|
|
const int32_t dst_shift,
|
|
const int32_t rhs_cols,
|
|
const int32_t rhs_rows,
|
|
const int32_t activation_min,
|
|
const int32_t activation_max);
|
|
|
|
/**
|
|
* @brief s8 Vector by Matrix (transposed) multiplication with s16 output
|
|
*
|
|
* @param[in] lhs Input left-hand side vector
|
|
* @param[in] rhs Input right-hand side matrix (transposed)
|
|
* @param[out] dst Output vector
|
|
* @param[in] lhs_offset Offset to be added to the input values of the left-hand side
|
|
* vector. Range: -127 to 128
|
|
* @param[in] rhs_offset Not used
|
|
* @param[in] scatter_offset Address offset for dst. First output is stored at 'dst', the
|
|
* second at 'dst + scatter_offset' and so on.
|
|
* @param[in] dst_multiplier Output multiplier
|
|
* @param[in] dst_shift Output shift
|
|
* @param[in] rhs_cols Number of columns in the right-hand side input matrix
|
|
* @param[in] rhs_rows Number of rows in the right-hand side input matrix
|
|
* @param[in] activation_min Minimum value to clamp the output to. Range: int16
|
|
* @param[in] activation_max Maximum value to clamp the output to. Range: int16
|
|
*
|
|
* @return The function returns <code>ARM_MATH_SUCCESS</code>
|
|
*
|
|
*/
|
|
arm_status arm_nn_vec_mat_mult_t_svdf_s8(const q7_t *lhs,
|
|
const q7_t *rhs,
|
|
q15_t *dst,
|
|
const int32_t lhs_offset,
|
|
const int32_t rhs_offset,
|
|
const int32_t scatter_offset,
|
|
const int32_t dst_multiplier,
|
|
const int32_t dst_shift,
|
|
const int32_t rhs_cols,
|
|
const int32_t rhs_rows,
|
|
const int32_t activation_min,
|
|
const int32_t activation_max);
|
|
|
|
/**
|
|
* @brief Depthwise convolution of transposed rhs matrix with 4 lhs matrices. To be used in padded cases where
|
|
* the padding is -lhs_offset(Range: int8). Dimensions are the same for lhs and rhs.
|
|
*
|
|
* @param[in] lhs Input left-hand side matrix
|
|
* @param[in] rhs Input right-hand side matrix (transposed)
|
|
* @param[in] lhs_offset LHS matrix offset(input offset). Range: -127 to 128
|
|
* @param[in] num_ch Number of channels in LHS/RHS
|
|
* @param[in] out_shift Per channel output shift. Length of vector is equal to number of channels
|
|
* @param[in] out_mult Per channel output multiplier. Length of vector is equal to number of channels
|
|
* @param[in] out_offset Offset to be added to the output values. Range: -127 to 128
|
|
* @param[in] activation_min Minimum value to clamp the output to. Range: int8
|
|
* @param[in] activation_max Maximum value to clamp the output to. Range: int8
|
|
* @param[in] row_x_col (row_dimension * col_dimension) of LHS/RHS matrix
|
|
* @param[in] output_bias Per channel output bias. Length of vector is equal to number of channels
|
|
* @param[in] out Output pointer
|
|
*
|
|
* @return The function returns one of the two
|
|
* - Updated output pointer if an implementation is available
|
|
* - NULL if no implementation is available.
|
|
*
|
|
* @note If number of channels is not a multiple of 4, upto 3 elements outside the boundary will be read
|
|
* out for the following.
|
|
* - Output shift
|
|
* - Output multiplier
|
|
* - Output bias
|
|
* - rhs
|
|
*/
|
|
q7_t *arm_nn_depthwise_conv_nt_t_padded_s8(const q7_t *lhs,
|
|
const q7_t *rhs,
|
|
const int32_t lhs_offset,
|
|
const uint16_t num_ch,
|
|
const int32_t *out_shift,
|
|
const int32_t *out_mult,
|
|
const int32_t out_offset,
|
|
const int32_t activation_min,
|
|
const int32_t activation_max,
|
|
const uint16_t row_x_col,
|
|
const int32_t *const output_bias,
|
|
q7_t *out);
|
|
|
|
/**
|
|
* @brief Depthwise convolution of transposed rhs matrix with 4 lhs matrices. To be used in non-padded cases.
|
|
* Dimensions are the same for lhs and rhs.
|
|
*
|
|
* @param[in] lhs Input left-hand side matrix
|
|
* @param[in] rhs Input right-hand side matrix (transposed)
|
|
* @param[in] lhs_offset LHS matrix offset(input offset). Range: -127 to 128
|
|
* @param[in] num_ch Number of channels in LHS/RHS
|
|
* @param[in] out_shift Per channel output shift. Length of vector is equal to number of channels.
|
|
* @param[in] out_mult Per channel output multiplier. Length of vector is equal to number of channels.
|
|
* @param[in] out_offset Offset to be added to the output values. Range: -127 to 128
|
|
* @param[in] activation_min Minimum value to clamp the output to. Range: int8
|
|
* @param[in] activation_max Maximum value to clamp the output to. Range: int8
|
|
* @param[in] row_x_col (row_dimension * col_dimension) of LHS/RHS matrix
|
|
* @param[in] output_bias Per channel output bias. Length of vector is equal to number of channels.
|
|
* @param[in] out Output pointer
|
|
*
|
|
* @return The function returns one of the two
|
|
* - Updated output pointer if an implementation is available
|
|
* - NULL if no implementation is available.
|
|
*
|
|
* @note If number of channels is not a multiple of 4, upto 3 elements outside the boundary will be read
|
|
* out for the following.
|
|
* - Output shift
|
|
* - Output multiplier
|
|
* - Output bias
|
|
* - rhs
|
|
*/
|
|
q7_t *arm_nn_depthwise_conv_nt_t_s8(const q7_t *lhs,
|
|
const q7_t *rhs,
|
|
const int32_t lhs_offset,
|
|
const uint16_t num_ch,
|
|
const int32_t *out_shift,
|
|
const int32_t *out_mult,
|
|
const int32_t out_offset,
|
|
const int32_t activation_min,
|
|
const int32_t activation_max,
|
|
const uint16_t row_x_col,
|
|
const int32_t *const output_bias,
|
|
q7_t *out);
|
|
|
|
/**
|
|
*@brief Matrix-multiplication function for convolution with reordered columns
|
|
*@param[in] pA pointer to operand A
|
|
*@param[in] pInBuffer pointer to operand B, always conssists of 2 vectors
|
|
*@param[in] ch_im_out numRow of A
|
|
*@param[in] numCol_A numCol of A
|
|
*@param[in] bias_shift amount of left-shift for bias
|
|
*@param[in] out_shift amount of right-shift for output
|
|
*@param[in] bias the bias
|
|
*@param[in,out] pOut pointer to output
|
|
*@return The function returns the incremented output pointer
|
|
*
|
|
*@details This function assumes that data in pInBuffer are reordered
|
|
*/
|
|
q7_t *arm_nn_mat_mult_kernel_q7_q15_reordered(const q7_t *pA,
|
|
const q15_t *pInBuffer,
|
|
const uint16_t ch_im_out,
|
|
const uint16_t numCol_A,
|
|
const uint16_t bias_shift,
|
|
const uint16_t out_shift,
|
|
const q7_t *bias,
|
|
q7_t *pOut);
|
|
|
|
/**
|
|
@brief Read 2 q15 elements and post increment pointer.
|
|
@param[in] in_q15 Pointer to pointer that holds address of input.
|
|
@return q31 value
|
|
*/
|
|
__STATIC_FORCEINLINE q31_t arm_nn_read_q15x2_ia(const q15_t **in_q15)
|
|
{
|
|
q31_t val;
|
|
|
|
memcpy(&val, *in_q15, 4);
|
|
*in_q15 += 2;
|
|
|
|
return (val);
|
|
}
|
|
|
|
/**
|
|
@brief Read 4 q7 from q7 pointer and post increment pointer.
|
|
@param[in] in_q7 Pointer to pointer that holds address of input.
|
|
@return q31 value
|
|
*/
|
|
__STATIC_FORCEINLINE q31_t arm_nn_read_q7x4_ia(const q7_t **in_q7)
|
|
{
|
|
q31_t val;
|
|
memcpy(&val, *in_q7, 4);
|
|
*in_q7 += 4;
|
|
|
|
return (val);
|
|
}
|
|
|
|
/**
|
|
@brief Read 2 q15 from q15 pointer.
|
|
@param[in] in_q15 pointer to address of input.
|
|
@return q31 value
|
|
*/
|
|
__STATIC_FORCEINLINE q31_t arm_nn_read_q15x2(const q15_t *in_q15)
|
|
{
|
|
q31_t val;
|
|
memcpy(&val, in_q15, 4);
|
|
|
|
return (val);
|
|
}
|
|
|
|
/**
|
|
@brief Read 4 q7 values.
|
|
@param[in] in_q7 pointer to address of input.
|
|
@return q31 value
|
|
*/
|
|
__STATIC_FORCEINLINE q31_t arm_nn_read_q7x4(const q7_t *in_q7)
|
|
{
|
|
q31_t val;
|
|
memcpy(&val, in_q7, 4);
|
|
|
|
return (val);
|
|
}
|
|
|
|
/**
|
|
@brief Write four q7 to q7 pointer and increment pointer afterwards.
|
|
@param[in] in Double pointer to input value
|
|
@param[in] value Four bytes to copy
|
|
*/
|
|
__STATIC_FORCEINLINE void arm_nn_write_q7x4_ia(q7_t **in, q31_t value)
|
|
{
|
|
memcpy(*in, &value, 4);
|
|
*in += 4;
|
|
}
|
|
|
|
/**
|
|
* @brief memset optimized for MVE
|
|
* @param[in, out] dst Destination pointer
|
|
* @param[in] val Value to set
|
|
* @param[in] block_size Number of bytes to copy.
|
|
*
|
|
*/
|
|
__STATIC_FORCEINLINE void arm_memset_q7(q7_t *dst, const q7_t val, uint32_t block_size)
|
|
{
|
|
#if defined(ARM_MATH_MVEI)
|
|
__asm volatile(" vdup.8 q0, %[set_val] \n"
|
|
" wlstp.8 lr, %[cnt], 1f \n"
|
|
"2: \n"
|
|
" vstrb.8 q0, [%[in]], #16 \n"
|
|
" letp lr, 2b \n"
|
|
"1: \n"
|
|
: [ in ] "+r"(dst)
|
|
: [ cnt ] "r"(block_size), [ set_val ] "r"(val)
|
|
: "q0", "memory", "r14");
|
|
#else
|
|
memset(dst, val, block_size);
|
|
#endif
|
|
}
|
|
|
|
#if defined(ARM_MATH_DSP)
|
|
|
|
/**
|
|
* @brief read and expand one q7 word into two q15 words
|
|
*/
|
|
|
|
__STATIC_FORCEINLINE const q7_t *read_and_pad(const q7_t *source, q31_t *out1, q31_t *out2)
|
|
{
|
|
q31_t inA = arm_nn_read_q7x4_ia(&source);
|
|
q31_t inAbuf1 = __SXTB16_RORn((uint32_t)inA, 8);
|
|
q31_t inAbuf2 = __SXTB16(inA);
|
|
|
|
#ifndef ARM_MATH_BIG_ENDIAN
|
|
*out2 = (int32_t)(__PKHTB(inAbuf1, inAbuf2, 16));
|
|
*out1 = (int32_t)(__PKHBT(inAbuf2, inAbuf1, 16));
|
|
#else
|
|
*out1 = (int32_t)(__PKHTB(inAbuf1, inAbuf2, 16));
|
|
*out2 = (int32_t)(__PKHBT(inAbuf2, inAbuf1, 16));
|
|
#endif
|
|
|
|
return source;
|
|
}
|
|
|
|
/**
|
|
* @brief read and expand one q7 word into two q15 words with reordering
|
|
*/
|
|
|
|
__STATIC_FORCEINLINE const q7_t *read_and_pad_reordered(const q7_t *source, q31_t *out1, q31_t *out2)
|
|
{
|
|
q31_t inA = arm_nn_read_q7x4_ia(&source);
|
|
#ifndef ARM_MATH_BIG_ENDIAN
|
|
*out2 = __SXTB16(__ROR((uint32_t)inA, 8));
|
|
*out1 = __SXTB16(inA);
|
|
#else
|
|
*out1 = __SXTB16(__ROR((uint32_t)inA, 8));
|
|
*out2 = __SXTB16(inA);
|
|
#endif
|
|
|
|
return source;
|
|
}
|
|
|
|
/**
|
|
* @brief read and expand one q7 word into two q15 words with reordering and add an offset
|
|
*/
|
|
__STATIC_FORCEINLINE const q7_t *
|
|
read_and_pad_reordered_with_offset(const q7_t *source, q31_t *out1, q31_t *out2, q31_t offset)
|
|
{
|
|
q31_t inA = arm_nn_read_q7x4_ia(&source);
|
|
|
|
#ifndef ARM_MATH_BIG_ENDIAN
|
|
*out2 = __SXTB16(__ROR((uint32_t)inA, 8));
|
|
*out1 = __SXTB16(inA);
|
|
#else
|
|
*out1 = __SXTB16(__ROR((uint32_t)inA, 8));
|
|
*out2 = __SXTB16(inA);
|
|
#endif
|
|
*out1 = __QADD16(*out1, offset);
|
|
*out2 = __QADD16(*out2, offset);
|
|
|
|
return source;
|
|
}
|
|
|
|
#endif
|
|
|
|
/**
|
|
* @defgroup NNBasicMath Basic Math Functions for Neural Network Computation
|
|
*
|
|
* Basic Math Functions for Neural Network Computation
|
|
*
|
|
*/
|
|
|
|
/**
|
|
* @brief q7 vector multiplication with variable output shifts
|
|
* @param[in] *pSrcA pointer to the first input vector
|
|
* @param[in] *pSrcB pointer to the second input vector
|
|
* @param[out] *pDst pointer to the output vector
|
|
* @param[in] out_shift amount of right-shift for output
|
|
* @param[in] blockSize number of samples in each vector
|
|
* @return none.
|
|
*
|
|
* <b>Scaling and Overflow Behavior:</b>
|
|
* \par
|
|
* The function uses saturating arithmetic.
|
|
* Results outside of the allowable q15 range [0x8000 0x7FFF] will be saturated.
|
|
*/
|
|
|
|
void arm_nn_mult_q15(q15_t *pSrcA, q15_t *pSrcB, q15_t *pDst, const uint16_t out_shift, uint32_t blockSize);
|
|
|
|
/**
|
|
* @brief q7 vector multiplication with variable output shifts
|
|
* @param[in] *pSrcA pointer to the first input vector
|
|
* @param[in] *pSrcB pointer to the second input vector
|
|
* @param[out] *pDst pointer to the output vector
|
|
* @param[in] out_shift amount of right-shift for output
|
|
* @param[in] blockSize number of samples in each vector
|
|
* @return none.
|
|
*
|
|
* <b>Scaling and Overflow Behavior:</b>
|
|
* \par
|
|
* The function uses saturating arithmetic.
|
|
* Results outside of the allowable q7 range [0x80 0x7F] will be saturated.
|
|
*/
|
|
|
|
void arm_nn_mult_q7(q7_t *pSrcA, q7_t *pSrcB, q7_t *pDst, const uint16_t out_shift, uint32_t blockSize);
|
|
|
|
/**
|
|
* @brief Matrix-multiplication function for convolution with per-channel requantization.
|
|
* @param[in] input_a pointer to operand A
|
|
* @param[in] input_b pointer to operand B, always consists of 2 vectors.
|
|
* @param[in] output_ch number of rows of A
|
|
* @param[in] out_shift pointer to per output channel requantization shift parameter.
|
|
* @param[in] out_mult pointer to per output channel requantization multiplier parameter.
|
|
* @param[in] out_offset output tensor offset.
|
|
* @param[in] activation_min minimum value to clamp the output to. Range : int8
|
|
* @param[in] activation_max maximum value to clamp the output to. Range : int8
|
|
* @param[in] num_col_a number of columns of A
|
|
* @param[in] output_bias per output channel bias. Range : int32
|
|
* @param[in,out] out_0 pointer to output
|
|
* @return The function returns one of the two
|
|
* 1. The incremented output pointer for a successful operation or
|
|
* 2. NULL if implementation is not available.
|
|
*
|
|
* @details This function does the matrix multiplication of weight matrix for all output channels
|
|
* with 2 columns from im2col and produces two elements/output_channel. The outputs are
|
|
* clamped in the range provided by activation min and max.
|
|
* Supported framework: TensorFlow Lite micro.
|
|
*/
|
|
q7_t *arm_nn_mat_mult_kernel_s8_s16(const q7_t *input_a,
|
|
const q15_t *input_b,
|
|
const uint16_t output_ch,
|
|
const int32_t *out_shift,
|
|
const int32_t *out_mult,
|
|
const int32_t out_offset,
|
|
const int16_t activation_min,
|
|
const int16_t activation_max,
|
|
const uint16_t num_col_a,
|
|
const int32_t *const output_bias,
|
|
q7_t *out_0);
|
|
|
|
/**
|
|
* @brief Common softmax function for s8 input and s8 or s16 output
|
|
* @param[in] input Pointer to the input tensor
|
|
* @param[in] num_rows Number of rows in the input tensor
|
|
* @param[in] row_size Number of elements in each input row
|
|
* @param[in] mult Input quantization multiplier
|
|
* @param[in] shift Input quantization shift within the range [0, 31]
|
|
* @param[in] diff_min Minimum difference with max in row. Used to check if
|
|
* the quantized exponential operation can be performed
|
|
* @param[in] int16_output Indicating s8 output if 0 else s16 output
|
|
* @param[out] output Pointer to the output tensor
|
|
*
|
|
* @note Supported framework: TensorFlow Lite micro (bit-accurate)
|
|
*
|
|
*/
|
|
void arm_nn_softmax_common_s8(const int8_t *input,
|
|
const int32_t num_rows,
|
|
const int32_t row_size,
|
|
const int32_t mult,
|
|
const int32_t shift,
|
|
const int32_t diff_min,
|
|
const bool int16_output,
|
|
void *output);
|
|
|
|
/**
|
|
* @brief macro for adding rounding offset
|
|
*/
|
|
#ifndef ARM_NN_TRUNCATE
|
|
#define NN_ROUND(out_shift) ((0x1 << out_shift) >> 1)
|
|
#else
|
|
#define NN_ROUND(out_shift) 0
|
|
#endif
|
|
|
|
// Macros for shortening quantization functions' names and avoid long lines
|
|
#define MUL_SAT(a, b) arm_nn_doubling_high_mult((a), (b))
|
|
#define MUL_SAT_MVE(a, b) arm_doubling_high_mult_mve_32x4((a), (b))
|
|
#define MUL_POW2(a, b) arm_nn_mult_by_power_of_two((a), (b))
|
|
|
|
#define DIV_POW2(a, b) arm_nn_divide_by_power_of_two((a), (b))
|
|
#define DIV_POW2_MVE(a, b) arm_divide_by_power_of_two_mve((a), (b))
|
|
|
|
#define EXP_ON_NEG(x) arm_nn_exp_on_negative_values((x))
|
|
#define ONE_OVER1(x) arm_nn_one_over_one_plus_x_for_x_in_0_1((x))
|
|
|
|
/**
|
|
* @brief Saturating doubling high multiply. Result matches
|
|
* NEON instruction VQRDMULH.
|
|
* @param[in] m1 Multiplicand. Range: {NN_Q31_MIN, NN_Q31_MAX}
|
|
* @param[in] m2 Multiplier. Range: {NN_Q31_MIN, NN_Q31_MAX}
|
|
* @return Result of multiplication.
|
|
*
|
|
*/
|
|
__STATIC_FORCEINLINE q31_t arm_nn_doubling_high_mult(const q31_t m1, const q31_t m2)
|
|
{
|
|
q31_t result = 0;
|
|
// Rounding offset to add for a right shift of 31
|
|
q63_t mult = 1 << 30;
|
|
|
|
if ((m1 < 0) ^ (m2 < 0))
|
|
{
|
|
mult = 1 - mult;
|
|
}
|
|
// Gets resolved as a SMLAL instruction
|
|
mult = mult + (q63_t)m1 * m2;
|
|
|
|
// Utilize all of the upper 32 bits. This is the doubling step
|
|
// as well.
|
|
result = (int32_t)(mult / (1ll << 31));
|
|
|
|
if ((m1 == m2) && (m1 == (int32_t)NN_Q31_MIN))
|
|
{
|
|
result = NN_Q31_MAX;
|
|
}
|
|
return result;
|
|
}
|
|
|
|
/**
|
|
* @brief Doubling high multiply without saturation. This is intended
|
|
* for requantization where the scale is a positive integer
|
|
*
|
|
* @param[in] m1 Multiplicand. Range: {NN_Q31_MIN, NN_Q31_MAX}
|
|
* @param[in] m2 Multiplier Range: {NN_Q31_MIN, NN_Q31_MAX}
|
|
* @return Result of multiplication.
|
|
* @note The result of this matches that of neon instruction
|
|
* VQRDMULH for m1 in range {NN_Q31_MIN, NN_Q31_MAX} and m2 in
|
|
* range {NN_Q31_MIN + 1, NN_Q31_MAX}. Saturation occurs when
|
|
* m1 equals m2 equals NN_Q31_MIN and that is not handled by
|
|
* this function.
|
|
*
|
|
*/
|
|
__STATIC_FORCEINLINE q31_t arm_nn_doubling_high_mult_no_sat(const q31_t m1, const q31_t m2)
|
|
{
|
|
q31_t result = 0;
|
|
union arm_nn_long_long mult;
|
|
|
|
// Rounding offset to add for a right shift of 31
|
|
mult.word.low = 1 << 30;
|
|
mult.word.high = 0;
|
|
|
|
// Gets resolved as a SMLAL instruction
|
|
mult.long_long = mult.long_long + (q63_t)m1 * m2;
|
|
|
|
// Utilize all of the upper 32 bits. This is the doubling step
|
|
// as well.
|
|
result = (int32_t)(mult.long_long >> 31);
|
|
|
|
return result;
|
|
}
|
|
|
|
/**
|
|
* @brief Rounding divide by power of two.
|
|
* @param[in] dividend - Dividend
|
|
* @param[in] exponent - Divisor = power(2, exponent)
|
|
* Range: [0, 31]
|
|
* @return Rounded result of division. Midpoint is rounded away from zero.
|
|
*
|
|
*/
|
|
__STATIC_FORCEINLINE q31_t arm_nn_divide_by_power_of_two(const q31_t dividend, const q31_t exponent)
|
|
{
|
|
q31_t result = 0;
|
|
const q31_t remainder_mask = (1 << exponent) - 1;
|
|
int32_t remainder = remainder_mask & dividend;
|
|
|
|
// Basic division
|
|
result = dividend >> exponent;
|
|
|
|
// Adjust 'result' for rounding (mid point away from zero)
|
|
q31_t threshold = remainder_mask >> 1;
|
|
if (result < 0)
|
|
{
|
|
threshold++;
|
|
}
|
|
if (remainder > threshold)
|
|
{
|
|
result++;
|
|
}
|
|
|
|
return result;
|
|
}
|
|
|
|
/**
|
|
* @brief Requantize a given value.
|
|
* @param[in] val Value to be requantized
|
|
* @param[in] multiplier multiplier. Range {NN_Q31_MIN + 1, Q32_MAX}
|
|
* @param[in] shift left or right shift for 'val * multiplier'
|
|
*
|
|
* @return Returns (val * multiplier)/(2 ^ shift)
|
|
*
|
|
*/
|
|
__STATIC_FORCEINLINE q31_t arm_nn_requantize(const q31_t val, const q31_t multiplier, const q31_t shift)
|
|
{
|
|
#ifdef CMSIS_NN_USE_SINGLE_ROUNDING
|
|
const int64_t total_shift = 31 - shift;
|
|
const int64_t new_val = val * (int64_t)multiplier;
|
|
|
|
int32_t result = new_val >> (total_shift - 1);
|
|
result = (result + 1) >> 1;
|
|
|
|
return result;
|
|
#else
|
|
return arm_nn_divide_by_power_of_two(arm_nn_doubling_high_mult_no_sat(val * (1 << LEFT_SHIFT(shift)), multiplier),
|
|
RIGHT_SHIFT(shift));
|
|
#endif
|
|
}
|
|
|
|
/**
|
|
* @brief Requantize a given 64 bit value.
|
|
* @param[in] val Value to be requantized in the range {-(1<<47)} to {(1<<47) - 1}
|
|
* @param[in] reduced_multiplier Reduced multiplier in the range {NN_Q31_MIN + 1, Q32_MAX} to {Q16_MIN + 1,
|
|
* Q16_MAX}
|
|
* @param[in] shift Left or right shift for 'val * multiplier' in the range {-31} to {7}
|
|
*
|
|
* @return Returns (val * multiplier)/(2 ^ shift)
|
|
*
|
|
*/
|
|
__STATIC_FORCEINLINE q31_t arm_nn_requantize_s64(const q63_t val, const q31_t reduced_multiplier, const q31_t shift)
|
|
{
|
|
const q63_t new_val = val * reduced_multiplier;
|
|
|
|
q31_t result = new_val >> (14 - shift); // 64->32 bit reduction
|
|
result = (result + 1) >> 1; // Last shift position and insert round
|
|
|
|
return result;
|
|
}
|
|
|
|
/**
|
|
* @brief memcpy optimized for MVE
|
|
* @param[in, out] dst Destination pointer
|
|
* @param[in] src Source pointer.
|
|
* @param[in] block_size Number of bytes to copy.
|
|
*
|
|
*/
|
|
__STATIC_FORCEINLINE void arm_memcpy_q7(q7_t *__RESTRICT dst, const q7_t *__RESTRICT src, uint32_t block_size)
|
|
{
|
|
#if defined(ARM_MATH_MVEI)
|
|
__asm volatile(" wlstp.8 lr, %[cnt], 1f \n"
|
|
"2: \n"
|
|
" vldrb.8 q0, [%[in]], #16 \n"
|
|
" vstrb.8 q0, [%[out]], #16 \n"
|
|
" letp lr, 2b \n"
|
|
"1: \n"
|
|
: [ in ] "+r"(src), [ out ] "+r"(dst)
|
|
: [ cnt ] "r"(block_size)
|
|
: "q0", "memory", "r14");
|
|
#else
|
|
memcpy(dst, src, block_size);
|
|
#endif
|
|
}
|
|
|
|
#if defined(ARM_MATH_MVEI)
|
|
/**
|
|
* @brief Vector saturating doubling high multiply returning high half.
|
|
* @param[in] m1 Multiplicand
|
|
* @param[in] m2 Multiplier
|
|
* @return Result of multiplication.
|
|
*
|
|
*/
|
|
__STATIC_FORCEINLINE int32x4_t arm_doubling_high_mult_mve(const int32x4_t m1, const q31_t m2)
|
|
{
|
|
return vqrdmulhq_n_s32(m1, m2);
|
|
}
|
|
|
|
/**
|
|
* @brief Vector rounding divide by power of two.
|
|
* @param[in] dividend - Dividend vector
|
|
* @param[in] exponent - Divisor = power(2, exponent)
|
|
* Range: [0, 31]
|
|
* @return Rounded result of division. Midpoint is rounded away from zero.
|
|
*
|
|
*/
|
|
__STATIC_FORCEINLINE int32x4_t arm_divide_by_power_of_two_mve(const int32x4_t dividend, const q31_t exponent)
|
|
{
|
|
const int32x4_t shift = vdupq_n_s32(-exponent);
|
|
const int32x4_t fixup = vshrq_n_s32(vandq_s32(dividend, shift), 31);
|
|
const int32x4_t fixed_up_dividend = vqaddq_s32(dividend, fixup);
|
|
return vrshlq_s32(fixed_up_dividend, shift);
|
|
}
|
|
|
|
/**
|
|
* @brief Requantize a given vector.
|
|
* @param[in] val Vector to be requantized
|
|
* @param[in] multiplier multiplier
|
|
* @param[in] shift shift
|
|
*
|
|
* @return Returns (val * multiplier)/(2 ^ shift)
|
|
*
|
|
*/
|
|
__STATIC_FORCEINLINE int32x4_t arm_requantize_mve(const int32x4_t val, const q31_t multiplier, const q31_t shift)
|
|
{
|
|
#ifdef CMSIS_NN_USE_SINGLE_ROUNDING
|
|
const int right_shift = MIN(-1, shift);
|
|
const int left_shift = shift - right_shift;
|
|
|
|
const int32x4_t left_shift_dup = vdupq_n_s32(left_shift);
|
|
const int32x4_t right_shift_dup = vdupq_n_s32(right_shift);
|
|
|
|
int32x4_t result = vqdmulhq_n_s32(vshlq_s32(val, left_shift_dup), multiplier);
|
|
result = vrshlq_s32(result, right_shift_dup);
|
|
|
|
return result;
|
|
#else
|
|
return arm_divide_by_power_of_two_mve(
|
|
arm_doubling_high_mult_mve(vshlq_s32(val, vdupq_n_s32(LEFT_SHIFT(shift))), multiplier), RIGHT_SHIFT(shift));
|
|
#endif
|
|
}
|
|
|
|
__STATIC_FORCEINLINE int32x4_t arm_doubling_high_mult_mve_32x4(const int32x4_t m1, const int32x4_t m2)
|
|
{
|
|
return vqrdmulhq_s32(m1, m2);
|
|
}
|
|
|
|
__STATIC_FORCEINLINE int32x4_t arm_divide_by_power_of_two_mve_32x4(const int32x4_t dividend, const int32x4_t exponent)
|
|
{
|
|
const int32x4_t shift = -exponent;
|
|
const int32x4_t fixup = vshrq_n_s32(vandq_s32(dividend, shift), 31);
|
|
const int32x4_t fixed_up_dividend = vqaddq_s32(dividend, fixup);
|
|
return vrshlq_s32(fixed_up_dividend, shift);
|
|
}
|
|
|
|
__STATIC_FORCEINLINE int32x4_t arm_requantize_mve_32x4(const int32x4_t val,
|
|
const int32x4_t multiplier,
|
|
const int32x4_t shift)
|
|
{
|
|
#ifdef CMSIS_NN_USE_SINGLE_ROUNDING
|
|
const int32x4_t right_shift = vminq_s32(vdupq_n_s32(-1), shift);
|
|
const int32x4_t left_shift = vqsubq_s32(shift, right_shift);
|
|
|
|
int32x4_t result = vqdmulhq_s32(vshlq_s32(val, left_shift), multiplier);
|
|
result = vrshlq_s32(result, right_shift);
|
|
|
|
return result;
|
|
#else
|
|
const int32x4_t zz = vdupq_n_s32(0);
|
|
const mve_pred16_t p = vcmpgtq_n_s32(shift, 0);
|
|
|
|
const int32x4_t left_shift = vpselq_s32(shift, zz, p);
|
|
const int32x4_t right_shift = -vpselq_s32(zz, shift, p);
|
|
|
|
return arm_divide_by_power_of_two_mve_32x4(arm_doubling_high_mult_mve_32x4(vshlq_s32(val, left_shift), multiplier),
|
|
right_shift);
|
|
#endif
|
|
}
|
|
#endif
|
|
|
|
// @note The following functions are used only for softmax layer, scaled bits = 5 assumed
|
|
|
|
__STATIC_FORCEINLINE int32_t arm_nn_exp_on_negative_values(int32_t val)
|
|
{
|
|
int32_t mask = 0;
|
|
int32_t shift = 24;
|
|
|
|
const int32_t val_mod_minus_quarter = (val & ((1 << shift) - 1)) - (1 << shift);
|
|
const int32_t remainder = val_mod_minus_quarter - val;
|
|
const int32_t x = (val_mod_minus_quarter << 5) + (1 << 28);
|
|
const int32_t x2 = MUL_SAT(x, x);
|
|
|
|
int32_t result = 1895147668 +
|
|
MUL_SAT(1895147668, x + DIV_POW2(MUL_SAT(DIV_POW2(MUL_SAT(x2, x2), 2) + MUL_SAT(x2, x), 715827883) + x2, 1));
|
|
|
|
#define SELECT_IF_NON_ZERO(x) \
|
|
{ \
|
|
mask = MASK_IF_NON_ZERO(remainder & (1 << shift++)); \
|
|
result = SELECT_USING_MASK(mask, MUL_SAT(result, x), result); \
|
|
}
|
|
|
|
SELECT_IF_NON_ZERO(1672461947)
|
|
SELECT_IF_NON_ZERO(1302514674)
|
|
SELECT_IF_NON_ZERO(790015084)
|
|
SELECT_IF_NON_ZERO(290630308)
|
|
SELECT_IF_NON_ZERO(39332535)
|
|
SELECT_IF_NON_ZERO(720401)
|
|
SELECT_IF_NON_ZERO(242)
|
|
|
|
#undef SELECT_IF_NON_ZERO
|
|
|
|
mask = MASK_IF_ZERO(val);
|
|
return SELECT_USING_MASK(mask, NN_Q31_MAX, result);
|
|
}
|
|
|
|
__STATIC_FORCEINLINE q31_t arm_nn_mult_by_power_of_two(const int32_t val, const int32_t exp)
|
|
{
|
|
const int32_t thresh = ((1 << (31 - exp)) - 1);
|
|
int32_t result = val << exp;
|
|
result = SELECT_USING_MASK(MASK_IF_NON_ZERO(val > thresh), NN_Q31_MAX, result);
|
|
result = SELECT_USING_MASK(MASK_IF_NON_ZERO(val < -thresh), NN_Q31_MIN, result);
|
|
return result;
|
|
}
|
|
|
|
__STATIC_FORCEINLINE int32_t arm_nn_one_over_one_plus_x_for_x_in_0_1(int32_t val)
|
|
{
|
|
const int64_t sum = (int64_t)val + (int64_t)NN_Q31_MAX;
|
|
const int32_t half_denominator = (int32_t)((sum + (sum >= 0 ? 1 : -1)) / 2L);
|
|
int32_t x = 1515870810 + MUL_SAT(half_denominator, -1010580540);
|
|
|
|
const int32_t shift = (1 << 29);
|
|
x += MUL_POW2(MUL_SAT(x, shift - MUL_SAT(half_denominator, x)), 2);
|
|
x += MUL_POW2(MUL_SAT(x, shift - MUL_SAT(half_denominator, x)), 2);
|
|
x += MUL_POW2(MUL_SAT(x, shift - MUL_SAT(half_denominator, x)), 2);
|
|
|
|
return MUL_POW2(x, 1);
|
|
}
|
|
|
|
/**
|
|
@brief Write 2 q15 elements and post increment pointer.
|
|
@param[in] dest_q15 Pointer to pointer that holds address of destination.
|
|
@param[in] src_q31 Input value to be written.
|
|
*/
|
|
__STATIC_FORCEINLINE void arm_nn_write_q15x2_ia(q15_t **dest_q15, q31_t src_q31)
|
|
{
|
|
q31_t val = src_q31;
|
|
|
|
memcpy(*dest_q15, &val, 4);
|
|
*dest_q15 += 2;
|
|
}
|
|
|
|
#ifdef __cplusplus
|
|
}
|
|
#endif
|
|
|
|
#endif
|