因为某个测试需要,在STM32F407平台上验证矩阵乘法,使用ARM官方库“CMSIS_5-5.5.1\CMSIS\DSP\Source\MatrixFunctions\arm_mat_mult_q31.c”中函数arm_mat_mult_q31。 测试实例,参照《安富莱_STM32-V5开发板_数字信号... 查看原文 true studio 问题汇总 ...
1) 矩阵乘法:矩阵乘法是神经网络中最重要的计算核心。本文是基于CMSIS-DSP中的mat_mult内核实现。与CMSIS实现类似,矩阵乘法核心是用2×2个内核实现,如Fig 2所示。这样可以重用一些数据并节省加载指令的总数。使用q31_t数据类型进行累加,并且两个操作数都是q15_t数据类型。我们用相应的偏置值初始化累加器。使用专用...
@@ -490,7 +490,7 @@ define dso_local void @arm_mat_mult_q31(i32* noalias nocapture readonly %A, i32* ; CHECK-NEXT: dls lr, r10 ; CHECK-NEXT: vmov.i32 q4, #0x0 ; CHECK-NEXT: vadd.i32 q5, q5, q0 ; CHECK-NEXT: vmlas.u32 q6, q2, r5 ; CHECK-NEXT: vmlas.i32 q6...
unsigned int row2, unsigned int col2) { if (col1 != row2) return false; arm_mat_init_f32(&mtrin1, row1, col1, m1); arm_mat_init_f32(&mtrin2, row2, col2, m2); arm_mat_init_f32(&mtrout, row1, col2, result); arm_mat_mult_f32(&mtrin1, &mtrin2, &mtrout); re...
case TEST_MAT_MULT_F32_1: Expand Down Expand Up @@ -149,5 +149,6 @@ a double precision computation. void BinaryTestsF32::tearDown(Testing::testID_t id,Client::PatternMgr *mgr) { (void)id; output.dump(mgr); } 3 changes: 2 additions & 1 deletion 3 Testing/Source/Tests/Binary...
239 + DSP_OBJ += arm_mat_mult_fast_q31.o 240 + DSP_OBJ += arm_mat_init_f32.o 241 + DSP_OBJ += arm_mat_mult_q31.o 242 + DSP_OBJ += arm_mat_add_q15.o 243 + DSP_OBJ += arm_mat_cmplx_mult_f32.o 244 + DSP_OBJ += arm_mat_add_f32.o 245 + DSP_OBJ ...
out = arm_nn_mat_mult_kernel_s8_s16(filter_data, buffer_a, output_ch, quant_params->shift, quant_params->multiplier, conv_params->output_offset, conv_params->activation.min, conv_params->activation.max, rhs_cols, rhs_cols, bias_data, out); im2col_buf = buffer_a; lhs_rows = 0;...
arm_mat_init_f32(&mtrin2, row2, col2, m2); arm_mat_init_f32(&mtrout, row1, col2, result); arm_mat_mult_f32(&mtrin1, &mtrin2, &mtrout); return true; } arm_mat_init_f32的定义在arm_math.h里面,如下 /** * 浮点矩阵初始化 * [in,out] S points to an instance of the...
arm_mat_cmplx_mult_q15.o ./ra/arm/CMSIS_5/CMSIS/DSP/Source/MatrixFunctions/arm_mat_cmplx_mult_q31.o ./ra/arm/CMSIS_5/CMSIS/DSP/Source/MatrixFunctions/arm_mat_cmplx_trans_f16.o ./ra/arm/CMSIS_5/CMSIS/DSP/Source/MatrixFunctions/arm_mat_cmplx_trans_f32.o ./ra/arm/CMSIS_5/CMSIS/...
Machine learning (ML) algorithms are moving to the IoT edge due to various considerations such as latency,power consumption, cost, network bandwidth, reliability,privacy and security. Hence, there is an increasing interest in developingneural network ...