Renesas
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。
Diff: cmsis_dsp/StatisticsFunctions/arm_rms_f32.c
- Revision:
- 1:fdd22bb7aa52
- Child:
- 2:da51fb522205
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/cmsis_dsp/StatisticsFunctions/arm_rms_f32.c Wed Nov 28 12:30:09 2012 +0000
@@ -0,0 +1,133 @@
+/* ----------------------------------------------------------------------
+* Copyright (C) 2010 ARM Limited. All rights reserved.
+*
+* $Date: 15. February 2012
+* $Revision: V1.1.0
+*
+* Project: CMSIS DSP Library
+* Title: arm_rms_f32.c
+*
+* Description: Root mean square value of an array of F32 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
+ */
+
+/**
+ * @defgroup RMS Root mean square (RMS)
+ *
+ *
+ * Calculates the Root Mean Sqaure of the elements in the input vector.
+ * The underlying algorithm is used:
+ *
+ * <pre>
+ * Result = sqrt(((pSrc[0] * pSrc[0] + pSrc[1] * pSrc[1] + ... + pSrc[blockSize-1] * pSrc[blockSize-1]) / blockSize));
+ * </pre>
+ *
+ * There are separate functions for floating point, Q31, and Q15 data types.
+ */
+
+/**
+ * @addtogroup RMS
+ * @{
+ */
+
+
+/**
+ * @brief Root Mean Square 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 rms value returned here
+ * @return none.
+ *
+ */
+
+void arm_rms_f32(
+ float32_t * pSrc,
+ uint32_t blockSize,
+ float32_t * pResult)
+{
+ float32_t sum = 0.0f; /* Accumulator */
+ float32_t in; /* Tempoprary variable to store input value */
+ uint32_t blkCnt; /* loop counter */
+
+#ifndef ARM_MATH_CM0
+
+ /* Run the below code for Cortex-M4 and Cortex-M3 */
+
+ /* 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[2] * A[2] + ... + A[blockSize-1] * A[blockSize-1] */
+ /* Compute sum of the squares and then store the result in a temporary variable, sum */
+ in = *pSrc++;
+ sum += in * in;
+ in = *pSrc++;
+ sum += in * in;
+ in = *pSrc++;
+ sum += in * in;
+ in = *pSrc++;
+ sum += 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;
+
+#else
+
+ /* Run the below code for Cortex-M0 */
+
+ /* Loop over blockSize number of values */
+ blkCnt = blockSize;
+
+#endif /* #ifndef ARM_MATH_CM0 */
+
+ while(blkCnt > 0u)
+ {
+ /* C = A[0] * A[0] + A[1] * A[1] + A[2] * A[2] + ... + A[blockSize-1] * A[blockSize-1] */
+ /* Compute sum of the squares and then store the results in a temporary variable, sum */
+ in = *pSrc++;
+ sum += in * in;
+
+ /* Decrement the loop counter */
+ blkCnt--;
+ }
+
+ /* Compute Rms and store the result in the destination */
+ arm_sqrt_f32(sum / (float32_t) blockSize, pResult);
+}
+
+/**
+ * @} end of RMS group
+ */