摘要
Graph convolutional neural networks(GCNs)have emerged as an effective approach to extending deep learning for graph data analytics,but they are computationally challenging given the irregular graphs and the large num-ber of nodes in a graph.GCNs involve chain sparse-dense matrix multiplications with six loops,which results in a large de-sign space for GCN accelerators.Prior work on GCN acceleration either employs limited loop optimization techniques,or determines the design variables based on random sampling,which can hardly exploit data reuse efficiently,thus degrading system efficiency.To overcome this limitation,this paper proposes GShuttle,a GCN acceleration scheme that maximizes memory access efficiency to achieve high performance and energy efficiency.GShuttle systematically explores loop opti-mization techniques for GCN acceleration,and quantitatively analyzes the design objectives(e.g.,required DRAM access-es and SRAM accesses)by analytical calculation based on multiple design variables.GShuttle further employs two ap-proaches,pruned search space sweeping and greedy search,to find the optimal design variables under certain design con-straints.We demonstrated the efficacy of GShuttle by evaluation on five widely used graph datasets.The experimental simulations show that GShuttle reduces the number of DRAM accesses by a factor of 1.5 and saves energy by a factor of 1.7 compared with the state-of-the-art approaches.
作者
李家军
王可
郑皓
Ahmed Louri
Jia-Jun Li;Ke Wang;Hao Zheng;Ahmed Louri(School of Astronautics,Beihang University,Beijing 100191,China;Department of Electrical and Computer Engineering,University of North Carolina at Charlotte,Charlotte,NC 28223 U.S.A.;Department of Electrical and Computer Engineering,University of Central Florida,Orlando,FL 32816,U.S.A.;Department of Electrical and Computer Engineering,George Washington University,Washington,DC 20052,U.S.A.)
基金
supported by the U.S.National Science Foundation under Grant Nos.CCF-2131946,CCF-1953980,and CCF-1702980.
