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370 lines
10 KiB
C
370 lines
10 KiB
C
/* ----------------------------------------------------------------------
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* Project: CMSIS DSP Library
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* Title: arm_svm_polynomial_predict_f16.c
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* Description: SVM Polynomial Classifier
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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/svm_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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#if !defined(ARM_MATH_MVE_FLOAT16) || defined(ARM_MATH_AUTOVECTORIZE)
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/*
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_Float16 is not supported in g++ so we avoid putting _Float16 definitions
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in the public headers.
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This function should at some point be moved in FastMath.
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*/
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__STATIC_INLINE float16_t arm_exponent_f16(float16_t x, int32_t nb)
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{
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float16_t r = x;
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nb --;
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while(nb > 0)
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{
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r = (_Float16)r * (_Float16)x;
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nb--;
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}
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return(r);
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}
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#endif
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/**
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* @addtogroup polysvm
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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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/**
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* @brief SVM polynomial prediction
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* @param[in] S Pointer to an instance of the polynomial SVM structure.
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* @param[in] in Pointer to input vector
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* @param[out] pResult Decision value
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* @return none.
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*
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*/
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void arm_svm_polynomial_predict_f16(
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const arm_svm_polynomial_instance_f16 *S,
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const float16_t * in,
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int32_t * pResult)
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{
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/* inlined Matrix x Vector function interleaved with dot prod */
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uint32_t numRows = S->nbOfSupportVectors;
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uint32_t numCols = S->vectorDimension;
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const float16_t *pSupport = S->supportVectors;
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const float16_t *pSrcA = pSupport;
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const float16_t *pInA0;
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const float16_t *pInA1;
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uint32_t row;
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uint32_t blkCnt; /* loop counters */
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const float16_t *pDualCoef = S->dualCoefficients;
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_Float16 sum = S->intercept;
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f16x8_t vSum = vdupq_n_f16(0.0f);
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row = numRows;
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/*
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* compute 4 rows in parrallel
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*/
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while (row >= 4) {
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const float16_t *pInA2, *pInA3;
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float16_t const *pSrcA0Vec, *pSrcA1Vec, *pSrcA2Vec, *pSrcA3Vec, *pInVec;
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f16x8_t vecIn, acc0, acc1, acc2, acc3;
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float16_t const *pSrcVecPtr = in;
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/*
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* Initialize the pointers to 4 consecutive MatrixA rows
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*/
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pInA0 = pSrcA;
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pInA1 = pInA0 + numCols;
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pInA2 = pInA1 + numCols;
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pInA3 = pInA2 + numCols;
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/*
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* Initialize the vector pointer
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*/
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pInVec = pSrcVecPtr;
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/*
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* reset accumulators
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*/
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acc0 = vdupq_n_f16(0.0f);
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acc1 = vdupq_n_f16(0.0f);
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acc2 = vdupq_n_f16(0.0f);
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acc3 = vdupq_n_f16(0.0f);
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pSrcA0Vec = pInA0;
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pSrcA1Vec = pInA1;
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pSrcA2Vec = pInA2;
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pSrcA3Vec = pInA3;
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blkCnt = numCols >> 3;
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while (blkCnt > 0U) {
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f16x8_t vecA;
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vecIn = vld1q(pInVec);
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pInVec += 8;
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vecA = vld1q(pSrcA0Vec);
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pSrcA0Vec += 8;
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acc0 = vfmaq(acc0, vecIn, vecA);
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vecA = vld1q(pSrcA1Vec);
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pSrcA1Vec += 8;
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acc1 = vfmaq(acc1, vecIn, vecA);
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vecA = vld1q(pSrcA2Vec);
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pSrcA2Vec += 8;
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acc2 = vfmaq(acc2, vecIn, vecA);
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vecA = vld1q(pSrcA3Vec);
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pSrcA3Vec += 8;
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acc3 = vfmaq(acc3, vecIn, vecA);
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blkCnt--;
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}
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/*
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* tail
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* (will be merged thru tail predication)
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*/
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blkCnt = numCols & 7;
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if (blkCnt > 0U) {
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mve_pred16_t p0 = vctp16q(blkCnt);
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f16x8_t vecA;
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vecIn = vldrhq_z_f16(pInVec, p0);
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vecA = vldrhq_z_f16(pSrcA0Vec, p0);
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acc0 = vfmaq(acc0, vecIn, vecA);
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vecA = vldrhq_z_f16(pSrcA1Vec, p0);
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acc1 = vfmaq(acc1, vecIn, vecA);
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vecA = vldrhq_z_f16(pSrcA2Vec, p0);
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acc2 = vfmaq(acc2, vecIn, vecA);
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vecA = vldrhq_z_f16(pSrcA3Vec, p0);
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acc3 = vfmaq(acc3, vecIn, vecA);
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}
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/*
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* Sum the partial parts
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*/
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f16x8_t vtmp = vuninitializedq_f16();
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vtmp = vsetq_lane(vecAddAcrossF16Mve(acc0), vtmp, 0);
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vtmp = vsetq_lane(vecAddAcrossF16Mve(acc1), vtmp, 1);
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vtmp = vsetq_lane(vecAddAcrossF16Mve(acc2), vtmp, 2);
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vtmp = vsetq_lane(vecAddAcrossF16Mve(acc3), vtmp, 3);
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vSum = vfmaq_m_f16(vSum, vld1q(pDualCoef),
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arm_vec_exponent_f16
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(vaddq_n_f16(vmulq_n_f16(vtmp, S->gamma), S->coef0),
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S->degree),vctp16q(4));
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pDualCoef += 4;
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pSrcA += numCols * 4;
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/*
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* Decrement the row loop counter
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*/
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row -= 4;
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}
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/*
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* compute 2 rows in parrallel
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*/
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if (row >= 2) {
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float16_t const *pSrcA0Vec, *pSrcA1Vec, *pInVec;
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f16x8_t vecIn, acc0, acc1;
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float16_t const *pSrcVecPtr = in;
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/*
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* Initialize the pointers to 2 consecutive MatrixA rows
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*/
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pInA0 = pSrcA;
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pInA1 = pInA0 + numCols;
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/*
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* Initialize the vector pointer
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*/
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pInVec = pSrcVecPtr;
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/*
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* reset accumulators
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*/
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acc0 = vdupq_n_f16(0.0f);
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acc1 = vdupq_n_f16(0.0f);
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pSrcA0Vec = pInA0;
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pSrcA1Vec = pInA1;
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blkCnt = numCols >> 3;
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while (blkCnt > 0U) {
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f16x8_t vecA;
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vecIn = vld1q(pInVec);
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pInVec += 8;
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vecA = vld1q(pSrcA0Vec);
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pSrcA0Vec += 8;
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acc0 = vfmaq(acc0, vecIn, vecA);
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vecA = vld1q(pSrcA1Vec);
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pSrcA1Vec += 8;
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acc1 = vfmaq(acc1, vecIn, vecA);
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blkCnt--;
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}
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/*
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* tail
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* (will be merged thru tail predication)
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*/
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blkCnt = numCols & 7;
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if (blkCnt > 0U) {
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mve_pred16_t p0 = vctp16q(blkCnt);
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f16x8_t vecA;
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vecIn = vldrhq_z_f16(pInVec, p0);
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vecA = vldrhq_z_f16(pSrcA0Vec, p0);
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acc0 = vfmaq(acc0, vecIn, vecA);
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vecA = vldrhq_z_f16(pSrcA1Vec, p0);
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acc1 = vfmaq(acc1, vecIn, vecA);
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}
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/*
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* Sum the partial parts
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*/
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f16x8_t vtmp = vuninitializedq_f16();
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vtmp = vsetq_lane(vecAddAcrossF16Mve(acc0), vtmp, 0);
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vtmp = vsetq_lane(vecAddAcrossF16Mve(acc1), vtmp, 1);
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vSum = vfmaq_m_f16(vSum, vld1q(pDualCoef),
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arm_vec_exponent_f16
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(vaddq_n_f16(vmulq_n_f16(vtmp, S->gamma), S->coef0), S->degree),
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vctp16q(2));
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pDualCoef += 2;
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pSrcA += numCols * 2;
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row -= 2;
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}
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if (row >= 1) {
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f16x8_t vecIn, acc0;
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float16_t const *pSrcA0Vec, *pInVec;
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float16_t const *pSrcVecPtr = in;
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/*
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* Initialize the pointers to last MatrixA row
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*/
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pInA0 = pSrcA;
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/*
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* Initialize the vector pointer
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*/
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pInVec = pSrcVecPtr;
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/*
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* reset accumulators
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*/
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acc0 = vdupq_n_f16(0.0f);
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pSrcA0Vec = pInA0;
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blkCnt = numCols >> 3;
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while (blkCnt > 0U) {
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f16x8_t vecA;
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vecIn = vld1q(pInVec);
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pInVec += 8;
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vecA = vld1q(pSrcA0Vec);
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pSrcA0Vec += 8;
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acc0 = vfmaq(acc0, vecIn, vecA);
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blkCnt--;
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}
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/*
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* tail
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* (will be merged thru tail predication)
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*/
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blkCnt = numCols & 7;
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if (blkCnt > 0U) {
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mve_pred16_t p0 = vctp16q(blkCnt);
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f16x8_t vecA;
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vecIn = vldrhq_z_f16(pInVec, p0);
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vecA = vldrhq_z_f16(pSrcA0Vec, p0);
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acc0 = vfmaq(acc0, vecIn, vecA);
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}
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/*
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* Sum the partial parts
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*/
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f16x8_t vtmp = vuninitializedq_f16();
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vtmp = vsetq_lane(vecAddAcrossF16Mve(acc0), vtmp, 0);
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vSum = vfmaq_m_f16(vSum, vld1q(pDualCoef),
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arm_vec_exponent_f16
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(vaddq_n_f16(vmulq_n_f16(vtmp, S->gamma), S->coef0), S->degree),
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vctp16q(1));
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}
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sum += (_Float16)vecAddAcrossF16Mve(vSum);
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*pResult = S->classes[STEP(sum)];
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}
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#else
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/**
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* @brief SVM polynomial prediction
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* @param[in] S Pointer to an instance of the polynomial SVM structure.
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* @param[in] in Pointer to input vector
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* @param[out] pResult Decision value
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* @return none.
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*
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*/
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void arm_svm_polynomial_predict_f16(
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const arm_svm_polynomial_instance_f16 *S,
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const float16_t * in,
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int32_t * pResult)
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{
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_Float16 sum=S->intercept;
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_Float16 dot=0;
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uint32_t i,j;
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const float16_t *pSupport = S->supportVectors;
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for(i=0; i < S->nbOfSupportVectors; i++)
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{
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dot=0;
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for(j=0; j < S->vectorDimension; j++)
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{
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dot = (_Float16)dot + (_Float16)in[j]* (_Float16)*pSupport++;
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}
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sum += (_Float16)S->dualCoefficients[i] * (_Float16)arm_exponent_f16((_Float16)S->gamma * (_Float16)dot + (_Float16)S->coef0, S->degree);
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}
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*pResult=S->classes[STEP(sum)];
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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 polysvm group
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*/
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#endif /* #if defined(ARM_FLOAT16_SUPPORTED) */
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