wwxy261/tiny_cnn

The convolution, relu and pooling is the basic units of the neural network. This test wants you to write the function of these three parts in C/C++: · Forward only, Backward is PLUS; · Support Conv2D, Pooling2D operator; Verify the results by test case and calculate the computation efficiency;

tiny_CNN性能测试

Convolution, Relu 以及Pooling是卷积神经网络的基本单元，这个项目采用C++实现了这三个基本单元的Forward以及Backward计算。并且实现了将这三个单元组合为一体的模块以提高性能，组合后有1.2倍的性能提升。该项目采用OpenMP支持多核并行。

目录

1. Introduction
2. Results

Introduction

1. 项目结构

./src 源码

./lib 开源第三方矩阵库Eigen(底层可调用MKL)

./data mnist数据集

./python numpy实现算法demo以及Pytorch搭建网络作为benchmark

./main.cpp 测试性能代码

2. Build

mkdir build
cd build
cmake ../
make

3. 算法简介

1. Convolution Caffe中经典的im2col算法实现

2. Convolution和MaxPooling实现中对最外层的n_sample循环加入parallel for优化

3. 将Convolution,Relu,Max_Pooling融合为一个模块，同样在最外层加入parallel for优化

4. 测试过程

使用mnist测试数据集输入，采用如下Pytorch定义的简化CNN网络模型(./python/mnist_cnn_pytorch.py)。

class Net(nn.Module):
   def __init__(self):
       super(Net, self).__init__()
       self.conv1 = nn.Conv2d(1, 6, 5)
       self.pool = nn.MaxPool2d(2, 2)
  

   def forward(self, x):
       x = x.view(-1, 1, 28, 28)
       x = self.pool(F.relu(self.conv1(x)))
       x = x.view(-1, 6 * 12 * 12)
       return x

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Results

1. 测试环境

Mac CPU(2 GHz 四核Intel Core i5)

HPC (2.5GHz 24核Core) ICC编译优化

2. Forward results

调用n次从网络输入到输出的forward计算并计时测试程序性能，结果如下表所示

n	Pytorch	serial	fuse	24-core	24-core-fuse	naive-numpy	fuse_numpy
n=1	0.582s	1.042s	0.995s	0.452s	0.431s	332.457s	64.329s
n=10	4.467s	7.357s	7.850s	1.433s	1.254s	-	-
n=100	42.883s	69.868s	77.798s	10.548s	9.938s	-	-

3. Backward results

调用n次从网络输出到输入的backward计算并计时测试程序性能,结果如下表所示

n	serial	fuse	24-core	24-core-fuse
n=1	1.082s	0.956s	0.470s	0.363s
n=10	9.861s	9.351s	4.496s	3.478s
n=100	97.247s	90.616s	41.811s	33.760s

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