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172
Drivers/CMSIS/DSP/Source/StatisticsFunctions/arm_logsumexp_f16.c
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172
Drivers/CMSIS/DSP/Source/StatisticsFunctions/arm_logsumexp_f16.c
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/* ----------------------------------------------------------------------
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* Project: CMSIS DSP Library
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* Title: arm_logsumexp_f16.c
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* Description: LogSumExp
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*
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* $Date: 23 April 2021
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* $Revision: V1.9.0
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*
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* Target Processor: Cortex-M and Cortex-A cores
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* -------------------------------------------------------------------- */
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/*
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* Copyright (C) 2010-2021 ARM Limited or its affiliates. All rights reserved.
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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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#include "dsp/statistics_functions_f16.h"
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#if defined(ARM_FLOAT16_SUPPORTED)
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#include <limits.h>
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#include <math.h>
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/**
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* @addtogroup LogSumExp
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* @{
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*/
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/**
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* @brief Computation of the LogSumExp
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*
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* In probabilistic computations, the dynamic of the probability values can be very
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* wide because they come from gaussian functions.
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* To avoid underflow and overflow issues, the values are represented by their log.
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* In this representation, multiplying the original exp values is easy : their logs are added.
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* But adding the original exp values is requiring some special handling and it is the
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* goal of the LogSumExp function.
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*
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* If the values are x1...xn, the function is computing:
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*
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* ln(exp(x1) + ... + exp(xn)) and the computation is done in such a way that
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* rounding issues are minimised.
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*
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* The max xm of the values is extracted and the function is computing:
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* xm + ln(exp(x1 - xm) + ... + exp(xn - xm))
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*
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* @param[in] *in Pointer to an array of input values.
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* @param[in] blockSize Number of samples in the input array.
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* @return LogSumExp
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*
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*/
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#if defined(ARM_MATH_MVE_FLOAT16) && !defined(ARM_MATH_AUTOVECTORIZE)
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#include "arm_helium_utils.h"
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#include "arm_vec_math_f16.h"
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float16_t arm_logsumexp_f16(const float16_t *in, uint32_t blockSize)
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{
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float16_t maxVal;
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const float16_t *pIn;
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int32_t blkCnt;
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_Float16 accum=0.0f16;
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_Float16 tmp;
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arm_max_no_idx_f16((float16_t *) in, blockSize, &maxVal);
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blkCnt = blockSize;
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pIn = in;
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f16x8_t vSum = vdupq_n_f16(0.0f16);
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blkCnt = blockSize >> 3;
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while(blkCnt > 0)
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{
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f16x8_t vecIn = vld1q(pIn);
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f16x8_t vecExp;
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vecExp = vexpq_f16(vsubq_n_f16(vecIn, maxVal));
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vSum = vaddq_f16(vSum, vecExp);
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/*
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* Decrement the blockSize loop counter
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* Advance vector source and destination pointers
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*/
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pIn += 8;
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blkCnt --;
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}
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/* sum + log */
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accum = vecAddAcrossF16Mve(vSum);
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blkCnt = blockSize & 0x7;
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while(blkCnt > 0)
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{
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tmp = *pIn++;
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accum += (_Float16)expf((float32_t)((_Float16)tmp - (_Float16)maxVal));
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blkCnt--;
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}
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accum = (_Float16)maxVal + (_Float16)logf((float32_t)accum);
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return (accum);
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}
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#else
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float16_t arm_logsumexp_f16(const float16_t *in, uint32_t blockSize)
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{
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_Float16 maxVal;
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_Float16 tmp;
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const float16_t *pIn;
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uint32_t blkCnt;
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_Float16 accum;
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pIn = in;
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blkCnt = blockSize;
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maxVal = *pIn++;
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blkCnt--;
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while(blkCnt > 0)
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{
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tmp = *pIn++;
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if (tmp > maxVal)
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{
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maxVal = tmp;
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}
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blkCnt--;
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}
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blkCnt = blockSize;
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pIn = in;
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accum = 0;
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while(blkCnt > 0)
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{
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tmp = *pIn++;
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accum += (_Float16)expf((float32_t)((_Float16)tmp - (_Float16)maxVal));
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blkCnt--;
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}
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accum = (_Float16)maxVal + (_Float16)logf((float32_t)accum);
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return(accum);
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}
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#endif /* defined(ARM_MATH_MVEF) && !defined(ARM_MATH_AUTOVECTORIZE) */
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/**
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* @} end of LogSumExp group
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*/
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#endif /* #if defined(ARM_FLOAT16_SUPPORTED) */
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