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A Unified Framework for Training, Mapping and Simulation of ReRAM-Based Convolutional Neural Network Acceleration

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A SystemC Simulator for ReRam-based Neural Network

This project is a simulator for ReRam Crossbar devices. It can be compiled using cmake and running on Windows/Linux.

An updated version called XB-Sim-Star can be found at CRAFT-THU/XB-Sim-Star.

When using this software, please use the following citation:

@ARTICLE{8676276,
  author={Liu, He and Han, Jianhui and Zhang, Youhui},
  journal={IEEE Computer Architecture Letters}, 
  title={A Unified Framework for Training, Mapping and Simulation of ReRAM-Based Convolutional Neural Network Acceleration}, 
  year={2019},
  volume={18},
  number={1},
  pages={63-66},
  doi={10.1109/LCA.2019.2908374}}

Content List

  • Install
  • Dataset
  • Run
  • Performance
  • Case study
  • Next work

   

Install

  • Install SystemC Library
    • For Linux，download the code to local directory and unzip the source code. Step into its directory, ~/systemc-2.3.2 e.g. and run following commands:
     Init build directory
kdir build
d build
 using cmake to compile systemc
make ..
ake
  • (Option)Install Cuda Library
    • Follow the instruction step on cuda website.
  • Configuration
    • Download this repo into your own path.
    • For linux，using Cmake to compile the code。This project has already provide CMakeLists.txt, users could change the link libraries according your own needs. Execute following command in shell:
     Init build directory
kdir build
d build
 using cmake to compile 
 default not using GPU
make .. -DCMAKE_BUILD_TYPE=Release
 or using GPU
make .. -DCMAKE_BUILD_TYPE=Release -DUSE_CUDA=on 
ake
 copy the executable file to upper directory and run
p simulator-windows ../
/simulator-windows

 

Dataset

  • The dataset used in this project is the Test part in cifar-10 dataset, 10000 pictures in total. And they are already transformed into 3*1024(32*32) and put into input.
  • Labels of Testset is labels.csv and put under source code directory.

 

Run

  • Parameter Configuration

    • Circuit parameters(config.h)
    DA Reference Voltage AD Reference Voltage DA width AD width Crossbar length Crossbar width Number of Crossbar in each Tile
    DA_V AD_V DA_WIDTH AD_WIDTH CROSSBAR_L CROSSBAR_W CROSSBAR_N
    • Neural Network parameters(config.h)
    Kernel size Input data size Channels of picture Size of picture Number of input picture pooling size
    KERNEL_SIZE INPUT_SIZE(KERNEL_SIZE*KERNEL_SIZE) CHANNELS_3/32/48/80/128 IMAGE_SIZE_32/16/8 PICTURE_NUM POOLING_SIZE_1/2/8

  • Code Generation of Neural Network Structure

    • This project generate codes by pre-defined template module using Python code cpp_gen.py. The template code of each module is:
    conv_cpp.template conv_buffer_cpp.template linear_cpp.template linear_buffer_cpp.template
    Convolution layer module template Buffer module template between convolution layer Fully connected layer module template Buffer module between fully connected layer

    The network needed structure list stored in cpp_gen.py and users can change this file to generate different NN structure

    • The code generation command is execute_process in CMakeLists.txt, and it is influenced by compile option USE_CUDA. Users don't have to run code generation command separately. The generated code will be put into generated directory.

    It will generate the headers of NN structure stage_conv_*.h, conv_buffer_*.h, stage_linear_*.h, linear_buffer_*.h.
    And they will be included in headers.cpp, using sc_signal to connect layers in main.cpp.

 

Performance

  • Accuracy
    • AD/DA module included：83.5%
    • with noise：82.4%

 

Case

  • Using VGG network to classify CIFAR-10 dataset
    • This project generated a VGG network by default, it contains 15 convolution layers and 2 fully connected layers, all of the weights (high dimension convolution kernel) have been transformed into 2-D matrix in CROSSBAR_L*CROSSBAR_W for hte computation of ReRam Crossbar.
    • All weight matrices has been transformed before and stored in weights. The weight conversion just like the following figure: avatar

   

Next

  • Support module replication
  • Multi Crossbars in each Tile

面向Crossbar的SystemC模拟器

本项目是面向Crossbar器件的模拟器，支持Visual Studio和cmake编译，可运行在Windows和Linux(Mac)下。

内容列表

  • 安装
  • 数据集
  • 运行
  • 性能
  • 样例
  • 下一步工作

   

安装

  • 安装SystemC库
    • 对于Windows用户，将systemc代码下载到本地之后，解压缩代码包。进入systemc文件夹，如E:\systemc-2.3.2\msvc10\SystemC中，用VS打开SystemC.vcxproj。对打开的项目分别用Debug模式和Release模式编译。
    • 对于Linux/Mac用户，将代码下载到本地之后，解压缩代码包。进入systemc文件夹，如~/systemc-2.3.2中，执行如下语句:
     创建build文件夹
kdir build
d build
 使用cmake对systemc编译
make ..
ake
  • (可选)安装Cuda库
    • 可参照cuda官网的的安装教程进行安装。
  • 配置工程文件
    • 对于Windows用户，使用Visual Studio进行开发，可根据本项目中提供的Visual Studio的配置文件进行配置。注意，如下配置须自行修改：
      (Debug和Release模式) 项目属性→C/C++→常规→附加包含目录→(SystemC库代码所在目录，如E:\systemc-2.3.2\src)
      (Debug模式) 项目属性→链接器→常规→附加库目录→(Debug库所在目录，如E:\systemc-2.3.2\msvc10\SystemC\Debug)
      (Release模式) 项目属性→链接器→常规→附加库目录→(Release库所在目录，如E:\systemc-2.3.2\msvc10\SystemC\Release)
    • 对于Linux/Mac用户，使用Cmake进行代码编译。本项目已提供CMakeLists.txt文件，可根据需要自行修改链接库的路径。编译代码时需执行如下语句：
     创建build文件夹
kdir build
d build
 使用cmake对systemc编译
 默认不使用GPU进行计算
make .. -DCMAKE_BUILD_TYPE=Release
 或者使用GPU计算
make .. -DCMAKE_BUILD_TYPE=Release -DUSE_CUDA=on 
ake
 将可执行文件拷贝到外层文件夹并执行
p simulator-windows ../
/simulator-windows

 

数据集

  • 本项目所用测试数据为cifar-10数据集的测试部分，总计10000张图片，已经将输入数据转换为3*1024(32*32)，并且放入input文件夹下。
  • 测试集标签为labels.csv文件，放在项目源代码目录下。

 

运行

  • 参数配置

    • 电路相关参数(config.h)
    DA参考电压 AD参考电压 DA宽度 AD宽度 Crossbar长度 Crossbar宽度 每个Tile中Crossbar个数
    DA_V AD_V DA_WIDTH AD_WIDTH CROSSBAR_L CROSSBAR_W CROSSBAR_N
    • 网络模型相关参数(config.h)
    卷积核大小 输入数据大小 图像通道数 图像尺寸 输入图片数目 池化大小
    KERNEL_SIZE INPUT_SIZE(KERNEL_SIZE*KERNEL_SIZE) CHANNELS_3/32/48/80/128 IMAGE_SIZE_32/16/8 PICTURE_NUM POOLING_SIZE_1/2/8

  • 网络结构代码生成

    • 本项目使用Python代码cpp_gen.py，根据预先写好的模块模板来生成代码。各模块的模板文件为如下：
    conv_cpp.template conv_buffer_cpp.template linear_cpp.template linear_buffer_cpp.template
    卷积层模块模板 卷积层间Buffer模块模板 全连接层模块模板 全连接层间Buffer模块模板

    在cpp_gen.py文件中，存放了所需网络的结构列表，可通过修改该文件来生成不同的网络结构。

    • 代码生成语句为CMakeLists.txt文件中的execute_process指令，受编译选项USE_CUDA影响，不需要单独执行代码生成指令。生成的代码将放入generated文件夹下。

    可以生成所需的网络结构头文件stage_conv_*.h, conv_buffer_*.h, stage_linear_*.h, linear_buffer_*.h.
    在headers.cpp文件中包含以上生成的头文件，并且根据各层之间的连接关系，在main.cpp文件中通过sc_signal进行串联。

 

性能

  • 准确率
    • 包括AD/DA模块：83.5%
    • 加入噪音：82.4%

 

样例

  • 使用VGG-16网络对CIFAR-10数据进行分类
    • 本项目默认生成了一个VGG-16网络，包括15个卷积层和2个全连接层，并且所有的权重（高维卷积核）全部转换为CROSSBAR_L*CROSSBAR_W的二维矩阵用于ReRam Crossbar计算。
    • 所有权重矩阵已经预先转换完成，并且存放在weights文件夹中。权重矩阵的转换方式如下图所示： avatar

   

下一步工作

  • 支持模块复用
  • 每个Tile中多个Crossbar的完善

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A Unified Framework for Training, Mapping and Simulation of ReRAM-Based Convolutional Neural Network Acceleration

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