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。


Revision:
1:fdd22bb7aa52
Child:
2:da51fb522205
diff -r 83d0537c7d84 -r fdd22bb7aa52 cmsis_dsp/StatisticsFunctions/arm_var_q15.c
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/cmsis_dsp/StatisticsFunctions/arm_var_q15.c	Wed Nov 28 12:30:09 2012 +0000
@@ -0,0 +1,180 @@
+/* ----------------------------------------------------------------------    
+* Copyright (C) 2010 ARM Limited. All rights reserved.    
+*    
+* $Date:        15. February 2012  
+* $Revision:     V1.1.0  
+*    
+* Project:         CMSIS DSP Library    
+* Title:        arm_var_q15.c    
+*    
+* Description:    Variance of an array of Q15 type.    
+*    
+* 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    
+ */
+
+/**    
+ * @addtogroup variance    
+ * @{    
+ */
+
+/**    
+ * @brief Variance of the elements of a Q15 vector.    
+ * @param[in]       *pSrc points to the input vector    
+ * @param[in]       blockSize length of the input vector    
+ * @param[out]      *pResult variance value returned here    
+ * @return none.    
+ *    
+ * @details    
+ * <b>Scaling and Overflow Behavior:</b>    
+ *    
+ * \par    
+ * The function is implemented using a 64-bit internal accumulator.    
+ * The input is represented in 1.15 format.   
+ * Intermediate multiplication yields a 2.30 format, and this    
+ * result is added without saturation to a 64-bit accumulator in 34.30 format.    
+ * With 33 guard bits in the accumulator, there is no risk of overflow, and the    
+ * full precision of the intermediate multiplication is preserved.    
+ * Finally, the 34.30 result is truncated to 34.15 format by discarding the lower     
+ * 15 bits, and then saturated to yield a result in 1.15 format.    
+ *    
+ */
+
+
+void arm_var_q15(
+  q15_t * pSrc,
+  uint32_t blockSize,
+  q31_t * pResult)
+{
+  q31_t sum = 0;                                 /* Accumulator */
+  q31_t meanOfSquares, squareOfMean;             /* Mean of square and square of mean */
+  q15_t mean;                                    /* mean */
+  uint32_t blkCnt;                               /* loop counter */
+  q15_t t;                                       /* Temporary variable */
+  q63_t sumOfSquares = 0;                        /* Accumulator */
+
+#ifndef ARM_MATH_CM0
+
+  /* Run the below code for Cortex-M4 and Cortex-M3 */
+
+  q31_t in;                                      /* Input variable */
+  q15_t in1;                                     /* Temporary variable */
+
+  /*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 = *__SIMD32(pSrc)++;
+    sum += ((in << 16) >> 16);
+    sum += (in >> 16);
+    sumOfSquares = __SMLALD(in, in, sumOfSquares);
+    in = *__SIMD32(pSrc)++;
+    sum += ((in << 16) >> 16);
+    sum += (in >> 16);
+    sumOfSquares = __SMLALD(in, in, sumOfSquares);
+
+    /* 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. */
+    in1 = *pSrc++;
+    sum += in1;
+    sumOfSquares = __SMLALD(in1, in1, sumOfSquares);
+
+    /* Decrement the loop counter */
+    blkCnt--;
+  }
+
+  /* Compute Mean of squares of the input samples    
+   * and then store the result in a temporary variable, meanOfSquares. */
+  t = (q15_t) ((1.0f / (float32_t) (blockSize - 1u)) * 16384);
+  sumOfSquares = __SSAT((sumOfSquares >> 15u), 16u);
+
+  meanOfSquares = (q31_t) ((sumOfSquares * t) >> 14u);
+
+#else
+
+  /* Run the below code for Cortex-M0 */
+
+  q15_t in;                                      /* Temporary variable */
+  /* 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[2] + ... + A[blockSize-1]) */
+    /* Compute sum of all input values and then store the result in a temporary variable, sum. */
+    sum += in;
+
+    /* Decrement the loop counter */
+    blkCnt--;
+  }
+
+  /* Compute Mean of squares of the input samples     
+   * and then store the result in a temporary variable, meanOfSquares. */
+  t = (q15_t) ((1.0f / (float32_t) (blockSize - 1u)) * 16384);
+  sumOfSquares = __SSAT((sumOfSquares >> 15u), 16u);
+  meanOfSquares = (q31_t) ((sumOfSquares * t) >> 14u);
+
+#endif /* #ifndef ARM_MATH_CM0 */
+
+  /* Compute mean of all input values */
+  t = (q15_t) ((1.0f / (float32_t) (blockSize * (blockSize - 1u))) * 32768);
+  mean = __SSAT(sum, 16u);
+
+  /* Compute square of mean */
+  squareOfMean = ((q31_t) mean * mean) >> 15;
+  squareOfMean = (q31_t) (((q63_t) squareOfMean * t) >> 15);
+
+  /* Compute variance and then store the result to the destination */
+  *pResult = (meanOfSquares - squareOfMean);
+
+}
+
+/**    
+ * @} end of variance group    
+ */