/* * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved. * * SPDX-License-Identifier: Apache-2.0 * * Licensed under the Apache License, Version 2.0 (the License); you may * not use this file except in compliance with the License. * You may obtain a copy of the License at * * www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an AS IS BASIS, WITHOUT * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. */ /* ---------------------------------------------------------------------- * Project: CMSIS NN Library * Title: arm_nnsupportfunctions.h * Description: Public header file of support functions for CMSIS NN Library * * $Date: 13. July 2018 * $Revision: V.1.0.0 * * Target Processor: Cortex-M cores * -------------------------------------------------------------------- */ #ifndef _ARM_NNSUPPORTFUNCTIONS_H_ #define _ARM_NNSUPPORTFUNCTIONS_H_ #include "arm_math.h" #include "arm_common_tables.h" //#include #ifdef __cplusplus extern "C" { #endif /** * @brief Union for SIMD access of Q31/Q15/Q7 types */ union arm_nnword { q31_t word; /**< Q31 type */ q15_t half_words[2]; /**< Q15 type */ q7_t bytes[4]; /**< Q7 type */ }; /** * @brief Struct for specifying activation function types * */ typedef enum { ARM_SIGMOID = 0, /**< Sigmoid activation function */ ARM_TANH = 1, /**< Tanh activation function */ } arm_nn_activation_type; /** * @defgroup nndata_convert Neural Network Data Conversion Functions * * Perform data type conversion in-between neural network operations * */ /** * @brief Converts the elements of the Q7 vector to Q15 vector without left-shift * @param[in] *pSrc points to the Q7 input vector * @param[out] *pDst points to the Q15 output vector * @param[in] blockSize length of the input vector * @return none. * */ void arm_q7_to_q15_no_shift(const q7_t * pSrc, q15_t * pDst, uint32_t blockSize); /** * @brief Converts the elements of the Q7 vector to reordered Q15 vector without left-shift * @param[in] *pSrc points to the Q7 input vector * @param[out] *pDst points to the Q15 output vector * @param[in] blockSize length of the input vector * @return none. * */ void arm_q7_to_q15_reordered_no_shift(const q7_t * pSrc, q15_t * pDst, uint32_t blockSize); #if defined (ARM_MATH_DSP) /** * @brief read and expand one Q7 word into two Q15 words */ __STATIC_FORCEINLINE void *read_and_pad(void *source, q31_t * out1, q31_t * out2) { q31_t inA = *__SIMD32(source)++; q31_t inAbuf1 = __SXTB16(__ROR(inA, 8)); q31_t inAbuf2 = __SXTB16(inA); #ifndef ARM_MATH_BIG_ENDIAN *out2 = __PKHTB(inAbuf1, inAbuf2, 16); *out1 = __PKHBT(inAbuf2, inAbuf1, 16); #else *out1 = __PKHTB(inAbuf1, inAbuf2, 16); *out2 = __PKHBT(inAbuf2, inAbuf1, 16); #endif return source; } /** * @brief read and expand one Q7 word into two Q15 words with reordering */ __STATIC_FORCEINLINE void *read_and_pad_reordered(void *source, q31_t * out1, q31_t * out2) { q31_t inA = *__SIMD32(source)++; #ifndef ARM_MATH_BIG_ENDIAN *out2 = __SXTB16(__ROR(inA, 8)); *out1 = __SXTB16(inA); #else *out1 = __SXTB16(__ROR(inA, 8)); *out2 = __SXTB16(inA); #endif 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. * * Scaling and Overflow Behavior: * \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. * * Scaling and Overflow Behavior: * \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 defition to adding rouding offset */ #ifndef ARM_NN_TRUNCATE #define NN_ROUND(out_shift) ( 0x1 << (out_shift - 1) ) #else #define NN_ROUND(out_shift) 0 #endif #ifdef __cplusplus } #endif #endif