CASIA OpenIR  > 国家专用集成电路设计工程技术研究中心
Accelerate Convolutional Neural Network with a customized VLIW DSP
Guo Peng1,2; Ma Hong1; Guo Ruoshan1; Liu Zhuang1; Li Pin1; Wang Donglin1
2018-08
Conference Name9th IEEE International Conference on Software Engineering and Service Science (ICSESS 2018)
Conference Date2018-10
Conference Place北京
Abstract

Convolutional neural networks (CNNs) have achieved outstanding performance in many domains. However, the stateof-the-art CNN models also introduce massive computation and huge memory footprint. To facilitate the deployment of CNN on embedded platforms, many existing studies focus on designing dedicated hardware accelerators. But there still exists many legacy DSP-based platforms which can also be exploited to accelerate the inference of CNN. In this work, we study the computation of CNN on MaPU, which is a customized VLIW DSP. MaPU is empowered with a multi-granularity parallel memory system and a flexible program model, which is very suitable for compute-intensive tasks. Through an in-depth analysis of CNN’s parallelism and the hardware architecture, we propose a kernel-expanded scheduling scheme, which can handle different kernel size uniformly. Based on our experiment on a face recognition network, MaPU achieves great performance and power efficiency.

Indexed BySCI
Language英语
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/23879
Collection国家专用集成电路设计工程技术研究中心
Corresponding AuthorGuo Peng
Affiliation1.中科院自动化研究所
2.中国科学院大学
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Guo Peng,Ma Hong,Guo Ruoshan,et al. Accelerate Convolutional Neural Network with a customized VLIW DSP[C],2018.
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