基于BWDSP众核的CNN计算任务划分优化
作者:
作者单位:

作者简介:

通讯作者:

中图分类号:

基金项目:

国家核高基重大专项(2012ZX01034-001-001)


Optimization of CNN Computing Task Partition Based on Many-Core BWDSP
Author:
Affiliation:

Fund Project:

  • 摘要
  • |
  • 图/表
  • |
  • 访问统计
  • |
  • 参考文献
  • |
  • 相似文献
  • |
  • 引证文献
  • |
  • 增强出版
  • |
  • 文章评论
    摘要:

    作为深度学习算法之一的卷积神经网络在多个领域有着重要的应用.因为其网络模型的规模和结构比较复杂,数据量较大,故需要考虑降低其对计算资源的要求.一般地,对于大数据量的计算任务,需要使用数据并行的方法进行任务的划分计算,而仅使用数据并行而对计算的任务的特点不加以结合,其数据传输量较高.因此需要通过对CNN网络结构及其计算特性的分析,设计合理的计算任务划分策略,减少数据的传输量.本文首先介绍了深度学习加速器中对计算任务的优化处理,接着介绍BWDSP的众核深度学习加速器的体系架构,并设计计算划分策略,基于VGGNet-16网络模型进行实验对比分析.实验结果表明该优化算法可以显著的提高数据传输的性能,降低数据的传输量.

    Abstract:

    Convolutional Neural Network (CNN), which is one of the deep learning algorithms, has been applied in many fields. Because the scale and structure of the network model are complex and the model has large amount of data, it is necessary to reduce the requirements for computational resource. Generally, it needs to use data parallel strategy to partition and calculate tasks with large amount of data. However, just using data parallel strategy which does not combine with the characteristics of computing tasks, it would result in high volume data transmission. Because of that, it is essential to design a reasonable data partitioning strategy for reducing the amount of data transmission through the analysis of the network structure and the computing characteristics of CNN. Firstly, this paper introduces the optimization of computing tasks in deep learning accelerator. Then, it introduces the architecture of the deep learning accelerator based on many-core BWDSP and designs the strategy of computing partition. And it compares and analyzes the experimental results based on VGGNet-16. The experimental results show that the proposed optimization algorithm can significantly improve the performance of data transmission and reduce the amount of data transmission.

    参考文献
    相似文献
    引证文献
引用本文

王改,郑启龙,邓文齐,杨江平,卢茂辉.基于BWDSP众核的CNN计算任务划分优化.计算机系统应用,2019,28(9):88-94

复制
分享
文章指标
  • 点击次数:
  • 下载次数:
  • HTML阅读次数:
  • 引用次数:
历史
  • 收稿日期:2019-02-28
  • 最后修改日期:2019-03-14
  • 录用日期:
  • 在线发布日期: 2019-09-09
  • 出版日期:
您是第位访问者
版权所有:中国科学院软件研究所 京ICP备05046678号-3
地址:北京海淀区中关村南四街4号 中科院软件园区 7号楼305房间,邮政编码:100190
电话:010-62661041 传真: Email:csa (a) iscas.ac.cn
技术支持:北京勤云科技发展有限公司

京公网安备 11040202500063号