devbox
很楠不爱3
这个屌丝很懒，什么也没留下！
关注作者
热门标签
热门文章
当前位置:   article > 正文

STM32F407-Discovery的硬件FPU_keil f407开启fpu

作者：很楠不爱3 | 2024-04-24 20:03:34

赞

踩

keil f407开启fpu

目录

1. STM32F407的FPU简介

2. 配置

3. 对比测试

3.1 实例1

3.2 实例2

4. 小结

本文基于STM32 HAL 库版本：STM32Cube_FW_F4_V1.27.0

1. STM32F407的FPU简介

FPU：Float Point Unit，也就是浮点运算单元。如果是定点 CPU（没有 FPU 的 CPU）计算浮点数，则按照 IEEE-754 标准，完成运算需要大量的指令，很耗费时间，几乎不能满足实时性的要求。但是对于有 FPU 的芯片来说，完成一个浮点运算或许只是几条指令就可以搞定，速度相对就快很多。

STM32F4具有有 32 位单精度硬件FPU[能加速计算float类型的数据]，支持浮点指令集，相对于Cortex M0 和 Cortex M3高出数十倍甚至上百倍的运算性能。

STM32F4通过配置协处理器控制寄存器（CPACR）就可以决定是否使用FPU，如果使用了FPU，遇到浮点运算，则自动开启硬件加速。

该寄存器描述如下图：

只需要把bit 20/21/22/23都配成1，即可开启FPU硬件加速。

2. 配置

文件：system_stm32f4xx.c，是否开启FPU由两个宏定义来进行管理：

__FPU_PRESENT：这个宏是指当前的IC是否具有FPU

__FPU_USED：是否开启FPU

__FPU_PRESENT在对应头文件中就有定义，这里是：stm32f407xx.h

是否开启取决于应用场景，只需要在MDK中配置即可：

Floating Point Hardware：选择Single Precision则自动关联到宏__FPU_USED且将其值置为1；如果选择Not Used，则宏__FPU_USED的值为0。

这里可以做个简单测试来验证，两种不同选择，在代码中打印出__FPU_USED的值进行对比。

main.c

  1. int main(void)
  2. {
  3.  
  4. 	/* STM32F4xx HAL library initialization:
  5. 	- Configure the Flash prefetch, instruction and Data caches
  6. 	- Configure the Systick to generate an interrupt each 1 msec
  7. 	- Set NVIC Group Priority to 4
  8. 	- Global MSP (MCU Support Package) initialization
  9. 	*/
  10. 	HAL_Init();
  11.  
  12. 	/* Configure LED3, LED4, LED5 and LED6 */
  13. 	//  BSP_LED_Init(LED3);
  14. 	//  BSP_LED_Init(LED4);
  15. 	//  BSP_LED_Init(LED5);
  16. 	BSP_LED_Init(LED6);
  17.  
  18. 	/* Configure the system clock to 168 MHz */
  19. 	SystemClock_Config();
  20.  
  21. 	/* 串口2初始化: 只用tx功能 */
  22. 	if(uart2_init(9600))
  23. 	{
  24. 		Error_Handler();
  25. 	}
  26.  
  27. 	printf("__CC_ARM:%d\n", __CC_ARM);
  28. 	printf("__FPU_PRESENT:%d\n", __FPU_PRESENT);
  29. 	printf("__FPU_USED:%d\n", __FPU_USED);
  30. 	printf("SCB->CPACR:0x%x\n", SCB->CPACR);
  31.  
  32. 	while(1){
  33. 		;
  34. 	}
  35.  
  36. 	return 0;
  37. }

两种选择，不同的打印，如下：

[16:15:54.205] __CC_ARM:1    //Floating Point Hardware: Single Precision

[16:15:54.221] __FPU_PRESENT:1

[16:15:54.237] __FPU_USED:1

[16:15:54.253] SCB->CPACR:0xf00000

[16:15:54.205] __CC_ARM:1    //Floating Point Hardware: Not used

[16:15:54.221] __FPU_PRESENT:1

[16:15:54.237] __FPU_USED:0

[16:15:54.253] SCB->CPACR:0x000000

注：改了配置要全编译。

解析文件core_cm4.h:

这个宏__CC_ARM是跟MDK绑定的：所以它一直都是有定义的

3. 对比测试

3.1 实例1

对比20000次的浮点数混合运算耗时。

main.c

  1. /* Includes ------------------------------------------------------------------*/
  2. #include "main.h"
  3.  
  4.  
  5. /* Private functions ---------------------------------------------------------*/
  6.  
  7. float re_sub;
  8. float a=0.14f;
  9. float b=0.26f;
  10.  
  11. void test_fpu_tmp1(void)
  12. {
  13. 	long i, j;
  14. 	float re_nul;
  15. 	for(i=0; i<10000; i++) {
  16. 		for(j=0; j<2; j++) {
  17. 			re_nul=a*b;
  18.         	re_sub=re_sub+re_nul;
  19. 			a=a+0.1f;
  20. 			b=b+0.1f;
  21. 		}
  22.     }
  23.  
  24.     //printf("res:%f\n", res);//打印很耗时
  25. }
  26.  
  27. int main(void)
  28. {
  29.  
  30. 	/* STM32F4xx HAL library initialization:
  31. 	- Configure the Flash prefetch, instruction and Data caches
  32. 	- Configure the Systick to generate an interrupt each 1 msec
  33. 	- Set NVIC Group Priority to 4
  34. 	- Global MSP (MCU Support Package) initialization
  35. 	*/
  36. 	HAL_Init();
  37.  
  38. 	/* Configure LED3, LED4, LED5 and LED6 */
  39. 	BSP_LED_Init(LED6);
  40.  
  41. 	/* Configure the system clock to 168 MHz */
  42. 	SystemClock_Config();
  43.  
  44. 	/* 串口2初始化: 只用tx功能 */
  45. 	if(uart2_init(9600))
  46. 	{
  47. 		Error_Handler();
  48. 	}
  49.  
  50. #if 1
  51. 	BSP_LED_Off(LED6);
  52. 	HAL_Delay(200);
  53.  
  54. 	BSP_LED_On(LED6);
  55. 	//HAL_Delay(1000);
  56. 	test_fpu_tmp1();
  57.  
  58. 	BSP_LED_Off(LED6);
  59. 	printf("re_sub:%f\n", re_sub);
  60. #endif
  61.  
  62. 	printf("__CC_ARM:%d\n", __CC_ARM);
  63. 	printf("__FPU_PRESENT:%d\n", __FPU_PRESENT);
  64. 	printf("__FPU_USED:%d\n", __FPU_USED);
  65. 	printf("SCB->CPACR:0x%x\n", SCB->CPACR);
  66.  
  67. 	while(1){
  68. 		;
  69. 	}
  70.  
  71. 	return 0;
  72. }
  73.

开启FPU：5ms左右

不开启FPU：26ms左右

开启FPU，调试的时候，看反汇编窗口，会看到xxx.F32，说明是使用FPU硬件加速的：

对比，不使用硬件FPU加速的情况：

注：（1）STM32F4的FPU的加减乘除运算指令分别是：

VADD.F32

VSUB.F32

VMUL.F32

VDIV.F32

（2）注意数据溢出问题，如果存在数据溢出，则程序运行会奇奇怪怪

（3）注意优化问题，比如下面这个测试程序：

  1. float a=0.14f;
  2. float b=0.26f;
  3. float re_nul;
  4. void test_fpu_tmp1(void)
  5. {
  6. 	long i, j;
  7. 	
  8. 	for(i=0; i<10000; i++) {
  9. 		for(j=0; j<2; j++) {
  10. 			re_nul=a*b;
  11.         	re_nul=re_nul+re_nul;
  12. 		}
  13.     }
  14.  
  15.     //printf("res:%f\n", res);//打印很耗时
  16. }
  17.

因为每次循环，re_nul的值都是一样，有可能根本不会循环那么多次(单步调试就可以认证这一点)，不知道是编译系统做了优化还是CPU运行的机制自己有优化。而且如果程序其他地方没有用到这个变量，也有可能被优化，另外注意的是打印是个很耗时的过程，因为串口通信是串行通信，测量代码运行时长不能包括打印语句。所以，编写测试程序，首先其他地方要用到这个变量，并且，变量的值要有变动，如下：

  1. float re_sub;
  2. float a=0.14f;
  3. float b=0.26f;
  4.  
  5. void test_fpu_tmp1(void)
  6. {
  7. 	long i, j;
  8. 	float re_nul;
  9. 	for(i=0; i<10000; i++) {
  10. 		for(j=0; j<2; j++) {
  11. 			re_nul=a*b;
  12.         	re_sub=re_sub+re_nul;
  13. 			a=a+0.1f;
  14. 			b=b+0.1f;
  15. 		}
  16.     }
  17.  
  18.     //printf("res:%f\n", res);//打印很耗时
  19. }
  20.  
  21. ..........................................
  22.  
  23. //在mian中将变量re_sub的值打印出来
  24. printf("re_sub:%f\n", re_sub);

3.2 实例2

如果运算量太小，可能是看不出什么差别的。比如：128个点的复数fft。

用户src目录下新增两个文件：complex.c和fft.c

complex.c：

  1. #include "complex.h"
  2.  
  3. void complex_add(complex a, complex b, complex *c)
  4. {
  5. 	c->real = a.real + b.real;
  6. 	c->img = a.img + b.img;
  7. }
  8.  
  9. void complex_sub(complex a, complex b, complex *c)
  10. {
  11. 	c->real = a.real - b.real;
  12. 	c->img = a.img - b.img;
  13. }
  14.  
  15. void complex_mul(complex a, complex b, complex *c)
  16. {
  17. 	c->real = a.real*b.real - a.img*b.img;
  18. 	c->img = a.real*b.img + a.img*b.real;
  19. }
  20.  
  21. void complex_div(complex   a, complex b, complex*c)
  22. {
  23. 	c->real = (a.real*b.real + a.img*b.img)/(b.real*b.real + b.img*b.img);
  24. 	c->img = (a.img*b.real - a.real*b.img)/(b.real*b.real + b.img*b.img);
  25. }

fft.c：

  1. #include <stdio.h>
  2. #include <math.h>
  3. #include "fft.h"
  4. #include "complex.h"
  5.  
  6. complex Wn, Temp, Res;
  7.  
  8. int L, B, P, K;
  9.  
  10. //输入序列的长度必须是2的整数次幂
  11. void Reader_Sort(complex *x, int len)
  12. {
  13. 	complex temp;
  14. 	int cur_rev = 0; //从0开始，递推出所有原自然排序数对应的倒序数
  15. 	int k = len / 2; //初始化权值系数
  16. 	int i, j;
  17. 	for (j = 1; j <= len - 1; j++) {
  18. 		//当前倒序数的最高位为0，
  19. 		if (cur_rev < k) {
  20. 			//把temp的最高位从0变为1(加上权系数)即可得到下一个倒序数
  21. 			cur_rev = cur_rev + k;
  22. 		} else {
  23. 			//当前倒序数的最高位为1
  24. 			while (cur_rev >= k) {
  25. 				//把最高位从1变为0(减去权系数即可)
  26. 				cur_rev = cur_rev - k;
  27. 				//没跳出循环就说明次高位为1，则更新权系数，将当前位设为0，......
  28. 				k = k / 2;
  29. 			}
  30. 			//当前位(最高位)为0，跳出while()，置为1(加上权系数)，即得当前倒序数
  31. 			cur_rev = cur_rev + k;
  32. 			//还原权系数值
  33. 			k = len / 2;
  34. 		}
  35. 		//printf("j=%d, cur_rev=%d\n", j, cur_rev);
  36. 		//互换x[j]和x[cur_rev]
  37. 		if (j < cur_rev) {
  38. 			//实数部分互换
  39. 			temp.real = x[j].real;
  40. 			x[j].real = x[cur_rev].real;
  41. 			x[cur_rev].real = temp.real;
  42.  
  43. 			//虚数部分互换
  44. 			temp.img = x[j].img;
  45. 			x[j].img = x[cur_rev].img;
  46. 			x[cur_rev].img = temp.img;
  47. 		}
  48. 	}
  49. }
  50.  
  51. void FFT(complex *input_seq, int SEQ_N, int SEQ_M, complex res_seq[])
  52. {
  53. 	int i, j, r;
  54. 	int L, B, K, P;
  55. 	complex Temp, Wn, Res;
  56. 	if (!input_seq) {
  57. 		printf("input sequence can be NULL\n");
  58. 		return ;
  59. 	}
  60.  
  61. 	Reader_Sort(input_seq, SEQ_N);
  62.  
  63. 	for (L=1; L <= SEQ_M; L++) {
  64. 		B=1;
  65. 		B=(int)pow(2, L-1);
  66. 		for (j=0; j<=B-1; j++) {
  67. 			K=(int)pow(2, SEQ_M-L);
  68. 			P=1;
  69. 			P=K*j;
  70. 			for (i=0; i<=K-1; i++) {
  71. 				r=j;
  72. 				r=j+2*B*i;
  73. 				Temp = input_seq[r];
  74. 				Wn.real = cos((2.0f*PI)/SEQ_N*P);
  75. 				Wn.img = -1.0f*sin((2.0f*PI)/SEQ_N*P);
  76. 				complex_mul(input_seq[r+B], Wn, &Res);
  77. 				input_seq[r].real=input_seq[r].real + Res.real;
  78. 				input_seq[r].img=input_seq[r].img + Res.img;
  79. 				input_seq[r+B].real=Temp.real - Res.real;
  80. 				input_seq[r+B].img=Temp.img - Res.img;
  81. 			}
  82. 		}
  83. 	}
  84.  
  85. 	if (!res_seq) {
  86. 		printf("result sequence is NULL\n");
  87. 		return ;
  88. 	} else {
  89. 		for(i=0; i<N; i++){
  90. 			res_seq[i].real = input_seq[i].real;
  91. 			res_seq[i].img = input_seq[i].img;
  92. 		}
  93. 	}
  94. }
  95.  
  96. void iFFT(complex *input_seq, int SEQ_N, int SEQ_M, complex res_seq[])
  97. {
  98. 	int i, j, r;
  99. 	int L, B, K, P;
  100. 	complex Temp, Wn, Res;
  101. 	if (!input_seq) {
  102. 		printf("input sequence can be NULL\n");
  103. 		return ;
  104. 	}
  105.  
  106. 	Reader_Sort(input_seq, SEQ_N);
  107.  
  108. 	for (L=1; L <= SEQ_M; L++) {
  109. 		B=1;
  110. 		B=(int)pow(2, L-1);
  111. 		for (j=0; j<=B-1; j++) {
  112. 			K=(int)pow(2, SEQ_M-L);
  113. 			P=1;
  114. 			P=K*j;
  115. 			for (i=0; i<=K-1; i++) {
  116. 				r=j;
  117. 				r=j+2*B*i;
  118. 				Temp = input_seq[r];
  119. 				Wn.real = cos((2.0f*PI)/SEQ_N*P);
  120. 				Wn.img = sin((2.0f*PI)/SEQ_N*P);
  121. 				complex_mul(input_seq[r+B], Wn, &Res);
  122. 				input_seq[r].real=input_seq[r].real + Res.real;
  123. 				input_seq[r].real=(1.0f/2.0f) * input_seq[r].real;
  124. 				input_seq[r].img=input_seq[r].img + Res.img;
  125. 				input_seq[r].img=(1.0f/2.0f) * input_seq[r].img;
  126. 				input_seq[r+B].real=Temp.real - Res.real;
  127. 				input_seq[r+B].real=(1.0f/2.0f) * input_seq[r+B].real;
  128. 				input_seq[r+B].img=Temp.img - Res.img;
  129. 				input_seq[r+B].img=(1.0f/2.0f) * input_seq[r+B].img;
  130. 			}
  131. 		}
  132. 	}
  133.  
  134. 	if (!res_seq) {
  135. 		printf("result sequence is NULL\n");
  136. 		return ;
  137. 	} else {
  138. 		for(i=0; i<N; i++){
  139. 			res_seq[i].real = input_seq[i].real;
  140. 			res_seq[i].img = input_seq[i].img;
  141. 		}
  142. 	}
  143. }
  144.

对应地用户目录inc下新增文件complex.h和fft.h

complex.h：

  1. #ifndef __COMPLEX_H_
  2. #define __COMPLEX_H_
  3.  
  4. typedef struct {
  5. 	float real;
  6. 	float img;
  7. } complex;
  8.  
  9. void complex_add(complex a, complex b, complex *c);
  10. void complex_sub(complex a, complex b, complex *c);
  11. void complex_mul(complex a, complex b, complex *c);
  12. void complex_div(complex   a, complex b, complex*c);
  13.  
  14. #endif

fft.h：

  1. #ifndef _FFT_H_
  2. #define _FFT_H_
  3. #include "complex.h"
  4.  
  5. #define PI (3.14159267f)
  6. /*
  7. 参数：
  8. （1）N=2^M
  9. （2）L=1~M，第L级
  10. （3）旋转因子的指数p，k是p的增量，p=p*k
  11. （4）B是 元素抽取间隔 = 运算的种类(旋转因子的种类)
  12. */
  13.  
  14. #define N (128)
  15. #define M (log(N)/log(2))
  16.  
  17. void FFT(complex *input_seq, int SEQ_N, int SEQ_M, complex res_seq[]);
  18. void iFFT(complex *input_seq, int SEQ_N, int SEQ_M, complex res_seq[]);
  19.  
  20. #endif

main.h中添加头文件

main.c：

  1. /* Includes ------------------------------------------------------------------*/
  2. #include "main.h"
  3.  
  4. complex INPUT_SEQ[N], RES_SEQ[N], OUTPUT_SEQ[N];
  5. float SEQ_DAT[N], dataR[N], dataI[N];
  6. int fft_test(void)
  7. {
  8. 	int i, j;
  9.  
  10. 	//构造实数序列
  11. 	for (i=0; i < N; i++) {
  12. 		SEQ_DAT[i]=i+0.0f;
  13. 	}
  14.  
  15. 	//构造虚数序列
  16. 	for (j=0; j<N; j++) {
  17. 		INPUT_SEQ[j].real= SEQ_DAT[j];
  18. 		INPUT_SEQ[j].img=0.0f;
  19.  
  20. 	}
  21.  
  22. //	for (i=0; i <N; i++) {
  23. //		printf("before fft: INPUT_SEQ[%d].real=%f, INPUT_SEQ[%d].img=%f\n", i, INPUT_SEQ[i].real, i, INPUT_SEQ[i].img);
  24. //	}
  25. //	printf("\n\n");
  26.  
  27. #if 1
  28. 	FFT(INPUT_SEQ, N, M, RES_SEQ);
  29. //	for (i=0; i <N; i++) {
  30. //		printf("fft: RES_SEQ[%d].real=%f, RES_SEQ[%d].img=%f\n", i, RES_SEQ[i].real, i, RES_SEQ[i].img);
  31. //	}
  32. //	printf("\n\n");
  33.  
  34. 	iFFT(RES_SEQ, N, M, OUTPUT_SEQ);
  35. #else
  36. 	HAL_Delay(1000);
  37. #endif
  38.  
  39. //	for (i=0; i <N; i++) { //打印很耗时
  40. //		printf("ifft: OUTPUT_SEQ[%d].real=%f, OUTPUT_SEQ[%d].img=%f\n", i, OUTPUT_SEQ[i].real, i, OUTPUT_SEQ[i].img);
  41. //	}
  42. //	printf("\n\n");
  43.  
  44. 	return 0;
  45. } 
  46.  
  47. int main(void)
  48. {
  49. 	HAL_Init();
  50.  
  51. 	BSP_LED_Init(LED6);
  52.  
  53. 	/* Configure the system clock to 168 MHz */
  54. 	SystemClock_Config();
  55.  
  56. 	/* 串口2初始化: 只用tx功能 */
  57. 	if(uart2_init(9600))
  58. 	{
  59. 		Error_Handler();
  60. 	}
  61.  
  62. #if 1
  63. 	BSP_LED_Off(LED6);
  64. 	HAL_Delay(200);
  65.  
  66. 	BSP_LED_On(LED6);
  67. 	//HAL_Delay(1000);
  68. 	//test_fpu_tmp1();
  69. 	fft_test();
  70. 	BSP_LED_Off(LED6);
  71. 	for (int i=0; i <N; i++) { //打印很耗时
  72. 		printf("ifft: OUTPUT_SEQ[%d].real=%f, OUTPUT_SEQ[%d].img=%f\n", i, OUTPUT_SEQ[i].real, i, OUTPUT_SEQ[i].img);
  73. 	}
  74. 	printf("\n\n");
  75. #endif 
  76.  
  77. 	while(1) {
  78. 		;
  79. 	}
  80.  
  81. 	return 0;
  82. }

配置使用FPU，则128个点的正反变换用时大概是0.2ms。

串口打印：逆变换后的数据

  1. [09:42:26.534] ifft: OUTPUT_SEQ[1].real=1.000000, OUTPUT_SEQ[1].img=0.000000
  2. [09:42:26.598] ifft: OUTPUT_SEQ[2].real=2.000000, OUTPUT_SEQ[2].img=0.000000
  3. [09:42:26.663] ifft: OUTPUT_SEQ[3].real=3.000000, OUTPUT_SEQ[3].img=0.000000
  4. [09:42:26.726] ifft: OUTPUT_SEQ[4].real=4.000000, OUTPUT_SEQ[4].img=0.000000
  5. [09:42:26.790] ifft: OUTPUT_SEQ[5].real=5.000000, OUTPUT_SEQ[5].img=0.000000
  6. [09:42:26.854] ifft: OUTPUT_SEQ[6].real=6.000000, OUTPUT_SEQ[6].img=0.000000
  7. [09:42:26.918] ifft: OUTPUT_SEQ[7].real=7.000000, OUTPUT_SEQ[7].img=0.000000
  8. [09:42:26.982] ifft: OUTPUT_SEQ[8].real=8.000000, OUTPUT_SEQ[8].img=0.000000
  9. [09:42:27.046] ifft: OUTPUT_SEQ[9].real=9.000000, OUTPUT_SEQ[9].img=0.000000
  10. [09:42:27.110] ifft: OUTPUT_SEQ[10].real=10.000000, OUTPUT_SEQ[10].img=0.000000
  11. [09:42:27.174] ifft: OUTPUT_SEQ[11].real=11.000000, OUTPUT_SEQ[11].img=0.000000
  12. [09:42:27.254] ifft: OUTPUT_SEQ[12].real=12.000000, OUTPUT_SEQ[12].img=0.000000
  13. [09:42:27.317] ifft: OUTPUT_SEQ[13].real=13.000000, OUTPUT_SEQ[13].img=0.000000
  14. [09:42:27.381] ifft: OUTPUT_SEQ[14].real=14.000000, OUTPUT_SEQ[14].img=0.000000
  15. [09:42:27.445] ifft: OUTPUT_SEQ[15].real=15.000000, OUTPUT_SEQ[15].img=0.000000
  16. [09:42:27.525] ifft: OUTPUT_SEQ[16].real=16.000000, OUTPUT_SEQ[16].img=0.000000
  17. [09:42:27.589] ifft: OUTPUT_SEQ[17].real=17.000000, OUTPUT_SEQ[17].img=0.000000
  18. [09:42:27.653] ifft: OUTPUT_SEQ[18].real=18.000000, OUTPUT_SEQ[18].img=0.000000
  19. [09:42:27.717] ifft: OUTPUT_SEQ[19].real=19.000000, OUTPUT_SEQ[19].img=0.000000
  20. [09:42:27.780] ifft: OUTPUT_SEQ[20].real=20.000000, OUTPUT_SEQ[20].img=0.000000
  21. [09:42:27.860] ifft: OUTPUT_SEQ[21].real=21.000000, OUTPUT_SEQ[21].img=0.000000
  22. [09:42:27.924] ifft: OUTPUT_SEQ[22].real=22.000000, OUTPUT_SEQ[22].img=0.000000
  23. [09:42:27.988] ifft: OUTPUT_SEQ[23].real=23.000000, OUTPUT_SEQ[23].img=0.000000
  24. [09:42:28.052] ifft: OUTPUT_SEQ[24].real=24.000000, OUTPUT_SEQ[24].img=0.000000
  25. [09:42:28.132] ifft: OUTPUT_SEQ[25].real=25.000000, OUTPUT_SEQ[25].img=0.000000
  26. [09:42:28.196] ifft: OUTPUT_SEQ[26].real=26.000000, OUTPUT_SEQ[26].img=0.000000
  27. [09:42:28.260] ifft: OUTPUT_SEQ[27].real=27.000000, OUTPUT_SEQ[27].img=0.000000
  28. [09:42:28.324] ifft: OUTPUT_SEQ[28].real=28.000000, OUTPUT_SEQ[28].img=0.000000
  29. [09:42:28.404] ifft: OUTPUT_SEQ[29].real=29.000000, OUTPUT_SEQ[29].img=0.000000
  30. [09:42:28.468] ifft: OUTPUT_SEQ[30].real=30.000000, OUTPUT_SEQ[30].img=0.000000
  31. [09:42:28.532] ifft: OUTPUT_SEQ[31].real=31.000000, OUTPUT_SEQ[31].img=0.000000
  32. [09:42:28.596] ifft: OUTPUT_SEQ[32].real=32.000000, OUTPUT_SEQ[32].img=0.000000
  33. [09:42:28.676] ifft: OUTPUT_SEQ[33].real=33.000000, OUTPUT_SEQ[33].img=0.000000
  34. [09:42:28.740] ifft: OUTPUT_SEQ[34].real=34.000000, OUTPUT_SEQ[34].img=0.000000
  35. [09:42:28.803] ifft: OUTPUT_SEQ[35].real=35.000000, OUTPUT_SEQ[35].img=0.000000
  36. [09:42:28.868] ifft: OUTPUT_SEQ[36].real=36.000000, OUTPUT_SEQ[36].img=0.000000
  37. [09:42:28.947] ifft: OUTPUT_SEQ[37].real=37.000000, OUTPUT_SEQ[37].img=0.000000
  38. [09:42:29.011] ifft: OUTPUT_SEQ[38].real=38.000000, OUTPUT_SEQ[38].img=0.000000
  39. [09:42:29.075] ifft: OUTPUT_SEQ[39].real=39.000000, OUTPUT_SEQ[39].img=0.000000
  40. [09:42:29.139] ifft: OUTPUT_SEQ[40].real=40.000000, OUTPUT_SEQ[40].img=0.000000
  41. [09:42:29.203] ifft: OUTPUT_SEQ[41].real=41.000000, OUTPUT_SEQ[41].img=0.000000
  42. [09:42:29.282] ifft: OUTPUT_SEQ[42].real=42.000000, OUTPUT_SEQ[42].img=0.000000
  43. [09:42:29.346] ifft: OUTPUT_SEQ[43].real=43.000000, OUTPUT_SEQ[43].img=0.000000
  44. [09:42:29.410] ifft: OUTPUT_SEQ[44].real=44.000000, OUTPUT_SEQ[44].img=0.000000
  45. [09:42:29.474] ifft: OUTPUT_SEQ[45].real=45.000000, OUTPUT_SEQ[45].img=0.000000
  46. [09:42:29.554] ifft: OUTPUT_SEQ[46].real=46.000000, OUTPUT_SEQ[46].img=0.000000
  47. [09:42:29.618] ifft: OUTPUT_SEQ[47].real=47.000000, OUTPUT_SEQ[47].img=0.000000
  48. [09:42:29.682] ifft: OUTPUT_SEQ[48].real=48.000000, OUTPUT_SEQ[48].img=0.000000
  49. [09:42:29.746] ifft: OUTPUT_SEQ[49].real=49.000000, OUTPUT_SEQ[49].img=0.000000
  50. [09:42:29.827] ifft: OUTPUT_SEQ[50].real=50.000000, OUTPUT_SEQ[50].img=0.000000
  51. [09:42:29.890] ifft: OUTPUT_SEQ[51].real=51.000000, OUTPUT_SEQ[51].img=0.000000
  52. [09:42:29.954] ifft: OUTPUT_SEQ[52].real=52.000000, OUTPUT_SEQ[52].img=0.000000
  53. [09:42:30.018] ifft: OUTPUT_SEQ[53].real=53.000000, OUTPUT_SEQ[53].img=0.000000
  54. [09:42:30.098] ifft: OUTPUT_SEQ[54].real=54.000000, OUTPUT_SEQ[54].img=0.000000
  55. [09:42:30.162] ifft: OUTPUT_SEQ[55].real=55.000000, OUTPUT_SEQ[55].img=0.000000
  56. [09:42:30.226] ifft: OUTPUT_SEQ[56].real=56.000000, OUTPUT_SEQ[56].img=0.000000
  57. [09:42:30.290] ifft: OUTPUT_SEQ[57].real=57.000000, OUTPUT_SEQ[57].img=0.000000
  58. [09:42:30.354] ifft: OUTPUT_SEQ[58].real=58.000000, OUTPUT_SEQ[58].img=0.000000
  59. [09:42:30.433] ifft: OUTPUT_SEQ[59].real=59.000000, OUTPUT_SEQ[59].img=0.000000
  60. [09:42:30.497] ifft: OUTPUT_SEQ[60].real=60.000000, OUTPUT_SEQ[60].img=0.000000
  61. [09:42:30.561] ifft: OUTPUT_SEQ[61].real=61.000000, OUTPUT_SEQ[61].img=0.000000
  62. [09:42:30.625] ifft: OUTPUT_SEQ[62].real=62.000000, OUTPUT_SEQ[62].img=0.000000
  63. [09:42:30.705] ifft: OUTPUT_SEQ[63].real=63.000000, OUTPUT_SEQ[63].img=0.000000
  64. [09:42:30.769] ifft: OUTPUT_SEQ[64].real=64.000000, OUTPUT_SEQ[64].img=0.000000
  65. [09:42:30.833] ifft: OUTPUT_SEQ[65].real=65.000000, OUTPUT_SEQ[65].img=0.000000
  66. [09:42:30.897] ifft: OUTPUT_SEQ[66].real=66.000000, OUTPUT_SEQ[66].img=0.000000
  67. [09:42:30.977] ifft: OUTPUT_SEQ[67].real=67.000000, OUTPUT_SEQ[67].img=0.000000
  68. [09:42:31.040] ifft: OUTPUT_SEQ[68].real=68.000000, OUTPUT_SEQ[68].img=0.000000
  69. [09:42:31.104] ifft: OUTPUT_SEQ[69].real=69.000000, OUTPUT_SEQ[69].img=0.000000
  70. [09:42:31.168] ifft: OUTPUT_SEQ[70].real=70.000000, OUTPUT_SEQ[70].img=0.000000
  71. [09:42:31.248] ifft: OUTPUT_SEQ[71].real=71.000000, OUTPUT_SEQ[71].img=0.000000
  72. [09:42:31.312] ifft: OUTPUT_SEQ[72].real=72.000000, OUTPUT_SEQ[72].img=0.000000
  73. [09:42:31.376] ifft: OUTPUT_SEQ[73].real=73.000000, OUTPUT_SEQ[73].img=0.000000
  74. [09:42:31.440] ifft: OUTPUT_SEQ[74].real=74.000000, OUTPUT_SEQ[74].img=0.000000
  75. [09:42:31.520] ifft: OUTPUT_SEQ[75].real=75.000000, OUTPUT_SEQ[75].img=0.000000
  76. [09:42:31.584] ifft: OUTPUT_SEQ[76].real=76.000000, OUTPUT_SEQ[76].img=0.000000
  77. [09:42:31.647] ifft: OUTPUT_SEQ[77].real=77.000000, OUTPUT_SEQ[77].img=0.000000
  78. [09:42:31.711] ifft: OUTPUT_SEQ[78].real=78.000000, OUTPUT_SEQ[78].img=0.000000
  79. [09:42:31.775] ifft: OUTPUT_SEQ[79].real=79.000000, OUTPUT_SEQ[79].img=0.000000
  80. [09:42:31.856] ifft: OUTPUT_SEQ[80].real=80.000000, OUTPUT_SEQ[80].img=0.000000
  81. [09:42:31.919] ifft: OUTPUT_SEQ[81].real=81.000000, OUTPUT_SEQ[81].img=0.000000
  82. [09:42:31.984] ifft: OUTPUT_SEQ[82].real=82.000000, OUTPUT_SEQ[82].img=0.000000
  83. [09:42:32.048] ifft: OUTPUT_SEQ[83].real=83.000000, OUTPUT_SEQ[83].img=0.000000
  84. [09:42:32.128] ifft: OUTPUT_SEQ[84].real=84.000000, OUTPUT_SEQ[84].img=0.000000
  85. [09:42:32.192] ifft: OUTPUT_SEQ[85].real=85.000000, OUTPUT_SEQ[85].img=0.000000
  86. [09:42:32.255] ifft: OUTPUT_SEQ[86].real=86.000000, OUTPUT_SEQ[86].img=0.000000
  87. [09:42:32.319] ifft: OUTPUT_SEQ[87].real=87.000000, OUTPUT_SEQ[87].img=0.000000
  88. [09:42:32.399] ifft: OUTPUT_SEQ[88].real=88.000000, OUTPUT_SEQ[88].img=0.000000
  89. [09:42:32.463] ifft: OUTPUT_SEQ[89].real=89.000000, OUTPUT_SEQ[89].img=0.000000
  90. [09:42:32.527] ifft: OUTPUT_SEQ[90].real=90.000000, OUTPUT_SEQ[90].img=0.000000
  91. [09:42:32.591] ifft: OUTPUT_SEQ[91].real=91.000000, OUTPUT_SEQ[91].img=0.000000
  92. [09:42:32.671] ifft: OUTPUT_SEQ[92].real=92.000000, OUTPUT_SEQ[92].img=0.000000
  93. [09:42:32.735] ifft: OUTPUT_SEQ[93].real=93.000000, OUTPUT_SEQ[93].img=0.000000
  94. [09:42:32.799] ifft: OUTPUT_SEQ[94].real=94.000000, OUTPUT_SEQ[94].img=0.000000
  95. [09:42:32.862] ifft: OUTPUT_SEQ[95].real=95.000000, OUTPUT_SEQ[95].img=0.000000
  96. [09:42:32.926] ifft: OUTPUT_SEQ[96].real=96.000000, OUTPUT_SEQ[96].img=0.000000
  97. [09:42:33.006] ifft: OUTPUT_SEQ[97].real=97.000000, OUTPUT_SEQ[97].img=0.000000
  98. [09:42:33.070] ifft: OUTPUT_SEQ[98].real=98.000000, OUTPUT_SEQ[98].img=0.000000
  99. [09:42:33.134] ifft: OUTPUT_SEQ[99].real=99.000000, OUTPUT_SEQ[99].img=0.000000
  100. [09:42:33.198] ifft: OUTPUT_SEQ[100].real=100.000000, OUTPUT_SEQ[100].img=0.000000
  101. [09:42:33.278] ifft: OUTPUT_SEQ[101].real=101.000000, OUTPUT_SEQ[101].img=0.000000
  102. [09:42:33.341] ifft: OUTPUT_SEQ[102].real=102.000000, OUTPUT_SEQ[102].img=0.000000
  103. [09:42:33.421] ifft: OUTPUT_SEQ[103].real=103.000000, OUTPUT_SEQ[103].img=0.000000
  104. [09:42:33.485] ifft: OUTPUT_SEQ[104].real=104.000000, OUTPUT_SEQ[104].img=0.000000
  105. [09:42:33.565] ifft: OUTPUT_SEQ[105].real=105.000000, OUTPUT_SEQ[105].img=0.000000
  106. [09:42:33.629] ifft: OUTPUT_SEQ[106].real=106.000000, OUTPUT_SEQ[106].img=0.000000
  107. [09:42:33.693] ifft: OUTPUT_SEQ[107].real=107.000000, OUTPUT_SEQ[107].img=0.000000
  108. [09:42:33.773] ifft: OUTPUT_SEQ[108].real=108.000000, OUTPUT_SEQ[108].img=0.000000
  109. [09:42:33.836] ifft: OUTPUT_SEQ[109].real=109.000000, OUTPUT_SEQ[109].img=0.000000
  110. [09:42:33.917] ifft: OUTPUT_SEQ[110].real=110.000000, OUTPUT_SEQ[110].img=0.000000
  111. [09:42:33.981] ifft: OUTPUT_SEQ[111].real=111.000000, OUTPUT_SEQ[111].img=0.000000
  112. [09:42:34.061] ifft: OUTPUT_SEQ[112].real=112.000000, OUTPUT_SEQ[112].img=0.000000
  113. [09:42:34.125] ifft: OUTPUT_SEQ[113].real=113.000000, OUTPUT_SEQ[113].img=0.000000
  114. [09:42:34.189] ifft: OUTPUT_SEQ[114].real=114.000000, OUTPUT_SEQ[114].img=0.000000
  115. [09:42:34.269] ifft: OUTPUT_SEQ[115].real=115.000000, OUTPUT_SEQ[115].img=0.000000
  116. [09:42:34.333] ifft: OUTPUT_SEQ[116].real=116.000000, OUTPUT_SEQ[116].img=0.000000
  117. [09:42:34.413] ifft: OUTPUT_SEQ[117].real=117.000000, OUTPUT_SEQ[117].img=0.000000
  118. [09:42:34.476] ifft: OUTPUT_SEQ[118].real=118.000000, OUTPUT_SEQ[118].img=0.000000
  119. [09:42:34.556] ifft: OUTPUT_SEQ[119].real=119.000000, OUTPUT_SEQ[119].img=0.000000
  120. [09:42:34.620] ifft: OUTPUT_SEQ[120].real=120.000000, OUTPUT_SEQ[120].img=0.000000
  121. [09:42:34.700] ifft: OUTPUT_SEQ[121].real=121.000000, OUTPUT_SEQ[121].img=0.000000
  122. [09:42:34.764] ifft: OUTPUT_SEQ[122].real=122.000000, OUTPUT_SEQ[122].img=0.000000
  123. [09:42:34.828] ifft: OUTPUT_SEQ[123].real=123.000000, OUTPUT_SEQ[123].img=0.000000
  124. [09:42:34.908] ifft: OUTPUT_SEQ[124].real=124.000000, OUTPUT_SEQ[124].img=0.000000
  125. [09:42:34.971] ifft: OUTPUT_SEQ[125].real=125.000000, OUTPUT_SEQ[125].img=0.000000
  126. [09:42:35.051] ifft: OUTPUT_SEQ[126].real=126.000000, OUTPUT_SEQ[126].img=0.000000
  127. [09:42:35.116] ifft: OUTPUT_SEQ[127].real=127.000000, OUTPUT_SEQ[127].img=0.000000

4. 小结

（1）使用FPU可以加快运算

（2）这个实例中的fft是浮点fft，并且是算法的直接实现，没有做任何优化，计算三角函数会耗时很长，实际应用过程中，可直接使用CMSIS的DSP库。里面的fft，iir/fir滤波器以及各种数学运算库都已经是针对ST系列芯片做了很好的优化，方便快速实现和开发算法[FPU+CMSIS-DSP]。

声明：本文内容由网友自发贡献，不代表【wpsshop博客】立场，版权归原作者所有，本站不承担相应法律责任。如您发现有侵权的内容，请联系我们。转载请注明出处：https://www.wpsshop.cn/w/很楠不爱3/article/detail/481312
推荐阅读
相关标签

Copyright © 2003-2013 www.wpsshop.cn 版权所有，并保留所有权利。

  

闽ICP备14008679号