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Rubik: A Hierarchical Architecture for Efficient Graph Neural Network Training

文献类型：期刊论文

作者Chen, Xiaobing3,4; Wang, Yuke1; Xie, Xinfeng5; Hu, Xing3; Basak, Abanti5; Liang, Ling5; Yan, Mingyu3; Deng, Lei2; Ding, Yufei1; Du, Zidong3
刊名IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS
出版日期2022-04-01
卷号41期号:4页码:936-949
ISSN号0278-0070
关键词Deep learning accelerator graph neural network (GNN)
DOI10.1109/TCAD.2021.3079142
英文摘要The graph convolutional network (GCN) emerges as a promising direction to learn the inductive representation in graph data commonly used in widespread applications, such as E-commerce, social networks, and knowledge graphs. However, learning from graphs is nontrivial because of its mixed computation model involving both graph analytics and neural network computing. To this end, we decompose the GCN learning into two hierarchical paradigms: 1) graph-level and 2) node-level computing. Such a hierarchical paradigm facilitates the software and hardware accelerations for GCN learning. We propose a lightweight graph reordering methodology, incorporated with a GCN accelerator architecture that equips a customized cache design to fully utilize the graph-level data reuse. We also propose a mapping methodology aware of data reuse and task-level parallelism to handle various graphs inputs effectively. The results show that Rubik accelerator design improves energy efficiency by 26.3x-1375.2x than GPU platforms across different datasets and GCN models.
资助项目National Key Research and Development Program of China[2017YFA0700902] ; NSF of China[61925208] ; NSF of China[62002338] ; NSF of China[61732007] ; NSF of China[61732002] ; NSF of China[61906179] ; NSF of China[U19B2019] ; NSF of China[U20A20227] ; Beijing Natural Science Foundation[JQ18013] ; Strategic Priority Research Program of Chinese Academy of Science[XDB32050200] ; Strategic Priority Research Program of Chinese Academy of Science[XDC05010300] ; Beijing Academy of Artificial Intelligence (BAAI) ; Beijing Nova Program of Science and Technology[Z191100001119093] ; Youth Innovation Promotion Association CAS ; Xplore Prize
WOS研究方向Computer Science ; Engineering
语种英语
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号WOS:000770597100014
源URL[http://119.78.100.204/handle/2XEOYT63/18938]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Hu, Xing
作者单位1.Univ Calif Santa Barbara, Dept Comp Sci, Santa Barbara, CA 93106 USA
2.Tsinghua Univ, Dept Precis Instrument, Ctr Brain Inspired Comp Res, Beijing 10084, Peoples R China
3.Chinese Acad Sci, Inst Comp Technol, State Key Lab Comp Architecture, Beijing 100190, Peoples R China
4.Univ Chinese Acad Sci, Sch Comp Sci & Technol, Beijing 100190, Peoples R China
5.Univ Calif Santa Barbara, Dept Elect & Comp Engn, Santa Barbara, CA 93106 USA
推荐引用方式
GB/T 7714		 Chen, Xiaobing,Wang, Yuke,Xie, Xinfeng,et al. Rubik: A Hierarchical Architecture for Efficient Graph Neural Network Training[J]. IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS,2022,41(4):936-949.
APA Chen, Xiaobing.,Wang, Yuke.,Xie, Xinfeng.,Hu, Xing.,Basak, Abanti.,...&Xie, Yuan.(2022).Rubik: A Hierarchical Architecture for Efficient Graph Neural Network Training.IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS,41(4),936-949.
MLA Chen, Xiaobing,et al."Rubik: A Hierarchical Architecture for Efficient Graph Neural Network Training".IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS 41.4(2022):936-949.

入库方式： OAI收割

来源：计算技术研究所

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