Fork of mbed-dsp. CMSIS-DSP library of supporting NEON
Dependents: mbed-os-example-cmsis_dsp_neon
Fork of mbed-dsp by
Information
Japanese version is available in lower part of this page.
このページの後半に日本語版が用意されています.
CMSIS-DSP of supporting NEON
What is this ?
A library for CMSIS-DSP of supporting NEON.
We supported the NEON to CMSIS-DSP Ver1.4.3(CMSIS V4.1) that ARM supplied, has achieved the processing speed improvement.
If you use the mbed-dsp library, you can use to replace this library.
CMSIS-DSP of supporting NEON is provied as a library.
Library Creation environment
CMSIS-DSP library of supporting NEON was created by the following environment.
- Compiler
ARMCC Version 5.03 - Compile option switch[C Compiler]
-DARM_MATH_MATRIX_CHECK -DARM_MATH_ROUNDING -O3 -Otime --cpu=Cortex-A9 --littleend --arm --apcs=/interwork --no_unaligned_access --fpu=vfpv3_fp16 --fpmode=fast --apcs=/hardfp --vectorize --asm
- Compile option switch[Assembler]
--cpreproc --cpu=Cortex-A9 --littleend --arm --apcs=/interwork --no_unaligned_access --fpu=vfpv3_fp16 --fpmode=fast --apcs=/hardfp
Effects of NEON support
In the data which passes to each function, large size will be expected more effective than small size.
Also if the data is a multiple of 16, effect will be expected in every function in the CMSIS-DSP.
NEON対応CMSIS-DSP
概要
NEON対応したCMSIS-DSPのライブラリです。
ARM社提供のCMSIS-DSP Ver1.4.3(CMSIS V4.1)をターゲットにNEON対応を行ない、処理速度向上を実現しております。
mbed-dspライブラリを使用している場合は、本ライブラリに置き換えて使用することができます。
NEON対応したCMSIS-DSPはライブラリで提供します。
ライブラリ作成環境
NEON対応CMSIS-DSPライブラリは、以下の環境で作成しています。
- コンパイラ
ARMCC Version 5.03 - コンパイルオプションスイッチ[C Compiler]
-DARM_MATH_MATRIX_CHECK -DARM_MATH_ROUNDING -O3 -Otime --cpu=Cortex-A9 --littleend --arm --apcs=/interwork --no_unaligned_access --fpu=vfpv3_fp16 --fpmode=fast --apcs=/hardfp --vectorize --asm
- コンパイルオプションスイッチ[Assembler]
--cpreproc --cpu=Cortex-A9 --littleend --arm --apcs=/interwork --no_unaligned_access --fpu=vfpv3_fp16 --fpmode=fast --apcs=/hardfp
NEON対応による効果について
CMSIS-DSP内の各関数へ渡すデータは、小さいサイズよりも大きいサイズの方が効果が見込めます。
また、16の倍数のデータであれば、CMSIS-DSP内のどの関数でも効果が見込めます。
cmsis_dsp/MatrixFunctions/arm_mat_mult_f32.c
- Committer:
- emilmont
- Date:
- 2013-05-30
- Revision:
- 2:da51fb522205
- Parent:
- 1:fdd22bb7aa52
- Child:
- 3:7a284390b0ce
File content as of revision 2:da51fb522205:
/* ---------------------------------------------------------------------- * Copyright (C) 2010 ARM Limited. All rights reserved. * * $Date: 15. February 2012 * $Revision: V1.1.0 * * Project: CMSIS DSP Library * Title: arm_mat_mult_f32.c * * Description: Floating-point matrix multiplication. * * Target Processor: Cortex-M4/Cortex-M3/Cortex-M0 * * Version 1.1.0 2012/02/15 * Updated with more optimizations, bug fixes and minor API changes. * * Version 1.0.10 2011/7/15 * Big Endian support added and Merged M0 and M3/M4 Source code. * * Version 1.0.3 2010/11/29 * Re-organized the CMSIS folders and updated documentation. * * Version 1.0.2 2010/11/11 * Documentation updated. * * Version 1.0.1 2010/10/05 * Production release and review comments incorporated. * * Version 1.0.0 2010/09/20 * Production release and review comments incorporated. * * Version 0.0.5 2010/04/26 * incorporated review comments and updated with latest CMSIS layer * * Version 0.0.3 2010/03/10 * Initial version * -------------------------------------------------------------------- */ #include "arm_math.h" /** * @ingroup groupMatrix */ /** * @defgroup MatrixMult Matrix Multiplication * * Multiplies two matrices. * * \image html MatrixMultiplication.gif "Multiplication of two 3 x 3 matrices" * Matrix multiplication is only defined if the number of columns of the * first matrix equals the number of rows of the second matrix. * Multiplying an <code>M x N</code> matrix with an <code>N x P</code> matrix results * in an <code>M x P</code> matrix. * When matrix size checking is enabled, the functions check: (1) that the inner dimensions of * <code>pSrcA</code> and <code>pSrcB</code> are equal; and (2) that the size of the output * matrix equals the outer dimensions of <code>pSrcA</code> and <code>pSrcB</code>. */ /** * @addtogroup MatrixMult * @{ */ /** * @brief Floating-point matrix multiplication. * @param[in] *pSrcA points to the first input matrix structure * @param[in] *pSrcB points to the second input matrix structure * @param[out] *pDst points to output matrix structure * @return The function returns either * <code>ARM_MATH_SIZE_MISMATCH</code> or <code>ARM_MATH_SUCCESS</code> based on the outcome of size checking. */ arm_status arm_mat_mult_f32( const arm_matrix_instance_f32 * pSrcA, const arm_matrix_instance_f32 * pSrcB, arm_matrix_instance_f32 * pDst) { float32_t *pIn1 = pSrcA->pData; /* input data matrix pointer A */ float32_t *pIn2 = pSrcB->pData; /* input data matrix pointer B */ float32_t *pInA = pSrcA->pData; /* input data matrix pointer A */ float32_t *pOut = pDst->pData; /* output data matrix pointer */ float32_t *px; /* Temporary output data matrix pointer */ float32_t sum; /* Accumulator */ uint16_t numRowsA = pSrcA->numRows; /* number of rows of input matrix A */ uint16_t numColsB = pSrcB->numCols; /* number of columns of input matrix B */ uint16_t numColsA = pSrcA->numCols; /* number of columns of input matrix A */ #ifndef ARM_MATH_CM0 /* Run the below code for Cortex-M4 and Cortex-M3 */ float32_t in1, in2, in3, in4; uint16_t col, i = 0u, j, row = numRowsA, colCnt; /* loop counters */ arm_status status; /* status of matrix multiplication */ #ifdef ARM_MATH_MATRIX_CHECK /* Check for matrix mismatch condition */ if((pSrcA->numCols != pSrcB->numRows) || (pSrcA->numRows != pDst->numRows) || (pSrcB->numCols != pDst->numCols)) { /* Set status as ARM_MATH_SIZE_MISMATCH */ status = ARM_MATH_SIZE_MISMATCH; } else #endif /* #ifdef ARM_MATH_MATRIX_CHECK */ { /* The following loop performs the dot-product of each row in pSrcA with each column in pSrcB */ /* row loop */ do { /* Output pointer is set to starting address of the row being processed */ px = pOut + i; /* For every row wise process, the column loop counter is to be initiated */ col = numColsB; /* For every row wise process, the pIn2 pointer is set ** to the starting address of the pSrcB data */ pIn2 = pSrcB->pData; j = 0u; /* column loop */ do { /* Set the variable sum, that acts as accumulator, to zero */ sum = 0.0f; /* Initiate the pointer pIn1 to point to the starting address of the column being processed */ pIn1 = pInA; /* Apply loop unrolling and compute 4 MACs simultaneously. */ colCnt = numColsA >> 2u; /* matrix multiplication */ while(colCnt > 0u) { /* c(m,n) = a(1,1)*b(1,1) + a(1,2) * b(2,1) + .... + a(m,p)*b(p,n) */ in3 = *pIn2; pIn2 += numColsB; in1 = pIn1[0]; in2 = pIn1[1]; sum += in1 * in3; in4 = *pIn2; pIn2 += numColsB; sum += in2 * in4; in3 = *pIn2; pIn2 += numColsB; in1 = pIn1[2]; in2 = pIn1[3]; sum += in1 * in3; in4 = *pIn2; pIn2 += numColsB; sum += in2 * in4; pIn1 += 4u; /* Decrement the loop count */ colCnt--; } /* If the columns of pSrcA is not a multiple of 4, compute any remaining MACs here. ** No loop unrolling is used. */ colCnt = numColsA % 0x4u; while(colCnt > 0u) { /* c(m,n) = a(1,1)*b(1,1) + a(1,2) * b(2,1) + .... + a(m,p)*b(p,n) */ sum += *pIn1++ * (*pIn2); pIn2 += numColsB; /* Decrement the loop counter */ colCnt--; } /* Store the result in the destination buffer */ *px++ = sum; /* Update the pointer pIn2 to point to the starting address of the next column */ j++; pIn2 = pSrcB->pData + j; /* Decrement the column loop counter */ col--; } while(col > 0u); #else /* Run the below code for Cortex-M0 */ float32_t *pInB = pSrcB->pData; /* input data matrix pointer B */ uint16_t col, i = 0u, row = numRowsA, colCnt; /* loop counters */ arm_status status; /* status of matrix multiplication */ #ifdef ARM_MATH_MATRIX_CHECK /* Check for matrix mismatch condition */ if((pSrcA->numCols != pSrcB->numRows) || (pSrcA->numRows != pDst->numRows) || (pSrcB->numCols != pDst->numCols)) { /* Set status as ARM_MATH_SIZE_MISMATCH */ status = ARM_MATH_SIZE_MISMATCH; } else #endif /* #ifdef ARM_MATH_MATRIX_CHECK */ { /* The following loop performs the dot-product of each row in pInA with each column in pInB */ /* row loop */ do { /* Output pointer is set to starting address of the row being processed */ px = pOut + i; /* For every row wise process, the column loop counter is to be initiated */ col = numColsB; /* For every row wise process, the pIn2 pointer is set ** to the starting address of the pSrcB data */ pIn2 = pSrcB->pData; /* column loop */ do { /* Set the variable sum, that acts as accumulator, to zero */ sum = 0.0f; /* Initialize the pointer pIn1 to point to the starting address of the row being processed */ pIn1 = pInA; /* Matrix A columns number of MAC operations are to be performed */ colCnt = numColsA; while(colCnt > 0u) { /* c(m,n) = a(1,1)*b(1,1) + a(1,2) * b(2,1) + .... + a(m,p)*b(p,n) */ sum += *pIn1++ * (*pIn2); pIn2 += numColsB; /* Decrement the loop counter */ colCnt--; } /* Store the result in the destination buffer */ *px++ = sum; /* Decrement the column loop counter */ col--; /* Update the pointer pIn2 to point to the starting address of the next column */ pIn2 = pInB + (numColsB - col); } while(col > 0u); #endif /* #ifndef ARM_MATH_CM0 */ /* Update the pointer pInA to point to the starting address of the next row */ i = i + numColsB; pInA = pInA + numColsA; /* Decrement the row loop counter */ row--; } while(row > 0u); /* Set status as ARM_MATH_SUCCESS */ status = ARM_MATH_SUCCESS; } /* Return to application */ return (status); } /** * @} end of MatrixMult group */