Hardware Acceleration for GCNs via Bidirectional Fusion

doi:10.1109/LCA.2021.3077956

	Hardware Acceleration for GCNs via Bidirectional Fusion
	Li, Han 3,4; Yan, Mingyu 3,4; Yang, Xiaocheng 4; Deng, Lei 2; Li, Wenming 4; Ye, Xiaochun 4; Fan, Dongrui 3,4; Xie, Yuan 1
刊名	IEEE COMPUTER ARCHITECTURE LETTERS
	2021
卷号	20 期号:1 页码:4
关键词	Random access memory Computational modeling Analytical models Hardware Engines Computer architecture Transforms Graph convolutional neural networks hardware accelerator bidirectional execution inter-phase fusion
ISSN号	1556-6056
DOI	10.1109/LCA.2021.3077956
英文摘要	Derived from the fusion of graph traversal and neural networks, graph convolutional neural networks (GCNs) have achieved state-of-the-art performance in graph learning. However, the hybrid execution pattern, caused by the opposite characteristics of graph traversal based phase and neural network based transformation phase, poses huge challenges to the efficient execution of traditional architectures. Although GCN accelerators have emerged to address these challenges, they fail to harvest both bidirectional execution and inter-phase fusion opportunities exposed by the alternate execution phases in GCNs. Previous works either concentrate on a single execution direction or exchange the execution order of phases without inter-phase fusion, hence failing to further improve performance and efficiency. Therefore, we propose a novel hardware unit named BiFusion, which can be easily applied to existing GCN accelerators with hybrid architecture in order to harvest both of the above opportunities. BiFusion enables dynamic direction selection and inter-phase fusion, and helps significantly reduce the amounts of data access and computation. Experiments show that integrating the BiFusion unit helps the state-of-the-art GCN accelerator achieve 2x speedup on average.
资助项目	National Natural Science Foundation of China[61732018] ; National Natural Science Foundation of China[61872335] ; National Natural Science Foundation of China[61802367]
WOS研究方向	Computer Science
语种	英语
出版者	IEEE COMPUTER SOC
WOS记录号	WOS:000658323400003
内容类型	期刊论文
源URL	[http://119.78.100.204/handle/2XEOYT63/17654]
专题	中国科学院计算技术研究所
通讯作者	Yan, Mingyu
作者单位	1.Univ Calif Santa Barbara, Santa Barbara, CA 93106 USA 2.Tsinghua Univ, Beijing 100084, Peoples R China 3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 4.Chinese Acad Sci, Inst Comp Technol, Beijing 100864, Peoples R China
推荐引用方式 GB/T 7714	Li, Han,Yan, Mingyu,Yang, Xiaocheng,et al. Hardware Acceleration for GCNs via Bidirectional Fusion[J]. IEEE COMPUTER ARCHITECTURE LETTERS,2021,20(1):4.
APA	Li, Han.,Yan, Mingyu.,Yang, Xiaocheng.,Deng, Lei.,Li, Wenming.,...&Xie, Yuan.(2021).Hardware Acceleration for GCNs via Bidirectional Fusion.IEEE COMPUTER ARCHITECTURE LETTERS,20(1),4.
MLA	Li, Han,et al."Hardware Acceleration for GCNs via Bidirectional Fusion".IEEE COMPUTER ARCHITECTURE LETTERS 20.1(2021):4.