Accelerating Graph Convolutional Networks Through a PIM-Accelerated Approach-Reference-Cited by-同舟云学术

Accelerating Graph Convolutional Networks Through a PIM-Accelerated Approach

Published:2023-09-01 Issue:9 Volume:72 Page:2628-2640
ISSN:0018-9340
Container-title:IEEE Transactions on Computers
language:
Short-container-title:IEEE Trans. Comput.

Author:

Jin Hai¹^ORCID,Chen Dan¹^ORCID,Zheng Long¹^ORCID,Huang Yu¹^ORCID,Yao Pengcheng¹,Zhao Jin¹^ORCID,Liao Xiaofei¹^ORCID,Jiang Wenbin¹^ORCID

Affiliation:

1. National Engineering Research Center for Big Data Technology and System, Services Computing Technology and System Lab, Clusters and Grid Computing Lab, School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei, China

Funder

National Natural Science Foundation of China

Huawei Technologies Co., Ltd

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computational Theory and Mathematics,Hardware and Architecture,Theoretical Computer Science,Software

Link

http://xplorestaging.ieee.org/ielx7/12/10213258/10070790.pdf?arnumber=10070790

Reference41 articles.

1. Roofline

2. Intel xeon e5–2680 v3,0

3. PCGCN: Partition-Centric Processing for Accelerating Graph Convolutional Network

4. HMC-Sim: A Simulation Framework for Hybrid Memory Cube Devices

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