CASIA OpenIR  > 国家专用集成电路设计工程技术研究中心
FBNA: A Fully Binarized Neural Network Accelerator
Guo Peng1,2; Hong Ma1; Ruizhi Chen1,2; Pin Li1; Shaolin Xie1; Donglin Wang1
2018-06
Conference NameThe International Conference on Field-Programmable Logic and Applications (FPL2018)
Conference Date2018-8
Conference Place爱尔兰都柏林
Abstract

In recent researches, binarized neural network
(BNN) has been proposed to address the massive computations
and large memory footprint problem of the convolutional neural
network (CNN). Several works have designed specific BNN
accelerators and showed very promising results. Nevertheless,
only part of the neural network is binarized in their architecture and the benefits of binary operations were not fully
exploited. In this work, we propose the first fully binarized
convolutional neural network accelerator (FBNA) architecture,
in which all convolutional operations are binarized and unified,
even including the first layer and padding. The fully unified
architecture provides more resource, parallelism and scalability
optimization opportunities. Compared with the state-of-the-art
BNN accelerator, our evaluation results show 3.1x performance,
5.4x resource efficiency and 4.9x power efficiency on CIFAR-10.
 

Indexed ByEI
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/23876
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,Hong Ma,Ruizhi Chen,et al. FBNA: A Fully Binarized Neural Network Accelerator[C],2018.
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