Fork of mbed-dsp. CMSIS-DSP library of supporting NEON

Dependents:   mbed-os-example-cmsis_dsp_neon

Fork of mbed-dsp by mbed official

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/StatisticsFunctions/arm_std_f32.c

Committer:
emilmont
Date:
2012-11-28
Revision:
1:fdd22bb7aa52
Child:
2:da51fb522205

File content as of revision 1:fdd22bb7aa52:

/* ----------------------------------------------------------------------    
* Copyright (C) 2010 ARM Limited. All rights reserved.    
*    
* $Date:        15. February 2012  
* $Revision:     V1.1.0  
*    
* Project:         CMSIS DSP Library    
* Title:        arm_std_f32.c    
*    
* Description:    Standard deviation of the elements of a floating-point vector.  
*    
* 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.    
* ---------------------------------------------------------------------------- */

#include "arm_math.h"

/**    
 * @ingroup groupStats    
 */

/**    
 * @defgroup STD Standard deviation    
 *    
 * Calculates the standard deviation of the elements in the input vector.     
 * The underlying algorithm is used:    
 *   
 * <pre>    
 *     Result = sqrt((sumOfSquares - sum<sup>2</sup> / blockSize) / (blockSize - 1))   
 *   
 *       where, sumOfSquares = pSrc[0] * pSrc[0] + pSrc[1] * pSrc[1] + ... + pSrc[blockSize-1] * pSrc[blockSize-1]   
 *   
 *                       sum = pSrc[0] + pSrc[1] + pSrc[2] + ... + pSrc[blockSize-1]   
 * </pre>   
 *    
 * There are separate functions for floating point, Q31, and Q15 data types.    
 */

/**    
 * @addtogroup STD    
 * @{    
 */


/**    
 * @brief Standard deviation of the elements of a floating-point vector.    
 * @param[in]       *pSrc points to the input vector    
 * @param[in]       blockSize length of the input vector    
 * @param[out]      *pResult standard deviation value returned here    
 * @return none.    
 *    
 */


void arm_std_f32(
  float32_t * pSrc,
  uint32_t blockSize,
  float32_t * pResult)
{
  float32_t sum = 0.0f;                          /* Temporary result storage */
  float32_t sumOfSquares = 0.0f;                 /* Sum of squares */
  float32_t in;                                  /* input value */
  uint32_t blkCnt;                               /* loop counter */

#ifndef ARM_MATH_CM0

  /* Run the below code for Cortex-M4 and Cortex-M3 */

  float32_t meanOfSquares, mean, squareOfMean;

  /*loop Unrolling */
  blkCnt = blockSize >> 2u;

  /* First part of the processing with loop unrolling.  Compute 4 outputs at a time.    
   ** a second loop below computes the remaining 1 to 3 samples. */
  while(blkCnt > 0u)
  {
    /* C = (A[0] * A[0] + A[1] * A[1] + ... + A[blockSize-1] * A[blockSize-1])  */
    /* Compute Sum of squares of the input samples    
     * and then store the result in a temporary variable, sum. */
    in = *pSrc++;
    sum += in;
    sumOfSquares += in * in;
    in = *pSrc++;
    sum += in;
    sumOfSquares += in * in;
    in = *pSrc++;
    sum += in;
    sumOfSquares += in * in;
    in = *pSrc++;
    sum += in;
    sumOfSquares += in * in;

    /* Decrement the loop counter */
    blkCnt--;
  }

  /* If the blockSize is not a multiple of 4, compute any remaining output samples here.    
   ** No loop unrolling is used. */
  blkCnt = blockSize % 0x4u;

  while(blkCnt > 0u)
  {
    /* C = (A[0] * A[0] + A[1] * A[1] + ... + A[blockSize-1] * A[blockSize-1]) */
    /* Compute Sum of squares of the input samples    
     * and then store the result in a temporary variable, sum. */
    in = *pSrc++;
    sum += in;
    sumOfSquares += in * in;

    /* Decrement the loop counter */
    blkCnt--;
  }

  /* Compute Mean of squares of the input samples    
   * and then store the result in a temporary variable, meanOfSquares. */
  meanOfSquares = sumOfSquares / ((float32_t) blockSize - 1.0f);

  /* Compute mean of all input values */
  mean = sum / (float32_t) blockSize;

  /* Compute square of mean */
  squareOfMean = (mean * mean) * (((float32_t) blockSize) /
                                  ((float32_t) blockSize - 1.0f));

  /* Compute standard deviation and then store the result to the destination */
  arm_sqrt_f32((meanOfSquares - squareOfMean), pResult);

#else

  /* Run the below code for Cortex-M0 */

  float32_t squareOfSum;                         /* Square of Sum */
  float32_t var;                                 /* Temporary varaince storage */

  /* Loop over blockSize number of values */
  blkCnt = blockSize;

  while(blkCnt > 0u)
  {
    /* C = (A[0] * A[0] + A[1] * A[1] + ... + A[blockSize-1] * A[blockSize-1]) */
    /* Compute Sum of squares of the input samples     
     * and then store the result in a temporary variable, sumOfSquares. */
    in = *pSrc++;
    sumOfSquares += in * in;

    /* C = (A[0] + A[1] + ... + A[blockSize-1]) */
    /* Compute Sum of the input samples     
     * and then store the result in a temporary variable, sum. */
    sum += in;

    /* Decrement the loop counter */
    blkCnt--;
  }

  /* Compute the square of sum */
  squareOfSum = ((sum * sum) / (float32_t) blockSize);

  /* Compute the variance */
  var = ((sumOfSquares - squareOfSum) / (float32_t) (blockSize - 1.0f));

  /* Compute standard deviation and then store the result to the destination */
  arm_sqrt_f32(var, pResult);

#endif /* #ifndef ARM_MATH_CM0 */

}

/**    
 * @} end of STD group    
 */