EnGN: A High-Throughput and Energy-Efficient Accelerator for Large Graph Neural Networks

Graph neural networks (GNNs) emerge as a powerful approach to process non-euclidean data structures and have been proved powerful in various application domains such as social networks and e-commerce. While such graph data maintained in real-world systems can be extremely large and sparse, thus empl...

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Veröffentlicht in:	IEEE transactions on computers Jg. 70; H. 9; S. 1511 - 1525
Hauptverfasser:	Liang, Shengwen, Wang, Ying, Liu, Cheng, He, Lei, Li, Huawei, Xu, Dawen, Li, Xiaowei
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.09.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	accelerator architecture Apexes Central processing units CPUs Data structures Energy efficiency Feature extraction Graph neural network Graph neural networks Graphics processing units Hardware hardware acceleration Memory management Neural networks Social networks System-on-chip Task analysis Tiling
ISSN:	0018-9340, 1557-9956
Online-Zugang:	Volltext
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