EnGN: A High-Throughput and Energy-Efficient Accelerator for Large Graph Neural Networks
文献类型：期刊论文
| 作者 | Liang, Shengwen1,2; Wang, Ying1,2; Liu, Cheng1,2; He, Lei1,2; Li, Huawei1,2; Xu, Dawen1,2; Li, Xiaowei1,2 |
| 刊名 | IEEE TRANSACTIONS ON COMPUTERS
 |
| 出版日期 | 2021-09-01 |
| 卷号 | 70期号:9页码:1511-1525 |
| 关键词 | Neural networks Hardware System-on-chip Task analysis Feature extraction Memory management Graph neural network accelerator architecture hardware acceleration |
| ISSN号 | 0018-9340 |
| DOI | 10.1109/TC.2020.3014632 |
| 英文摘要 | 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 employing GNNs to deal with them requires substantial computational and memory overhead, which induces considerable energy and resource cost on CPUs and GPUs. In this article, we present a specialized accelerator architecture, EnGN, to enable high-throughput and energy-efficient processing of large-scale GNNs. The proposed EnGN is designed to accelerate the three key stages of GNN propagation, which is abstracted as common computing patterns shared by typical GNNs. To support the key stages simultaneously, we propose the ring-edge-reduce(RER) dataflow that tames the poor locality of sparsely-and-randomly connected vertices, and the RER PE-array to practice RER dataflow. In addition, we utilize a graph tiling strategy to fit large graphs into EnGN and make good use of the hierarchical on-chip buffers through adaptive computation reordering and tile scheduling. Overall, EnGN achieves performance speedup by 1802.9X, 19.75X, and 2.97X and energy efficiency by 1326.35X, 304.43X, and 6.2X on average compared to CPU, GPU, and a state-of-the-art GCN accelerator HyGCN, respectively. |
| 资助项目 | National Key Research and Development Program of China[2018AAA0102700] ; National Natural Science Foundation of China[61874124] ; National Natural Science Foundation of China[61876173] ; National Natural Science Foundation of China[61532017] ; National Natural Science Foundation of China[61772300] ; National Natural Science Foundation of China[61902375] ; Strategic Priority Research Program of Chinese Academy of Sciences[XDC05030201] ; Strategic Priority Research Program of Chinese Academy of Sciences[YESS2016qnrc001] |
| WOS研究方向 | Computer Science ; Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:000682123200015 |
| 出版者 | IEEE COMPUTER SOC |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/17387]  |
| 专题 | 中国科学院计算技术研究所期刊论文_英文 |
| 通讯作者 | Wang, Ying; Li, Huawei |
| 作者单位 | 1.Chinese Acad Sci, State Key Lab Comp Architecture, Inst Comp Technol, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100190, Peoples R China |
| 推荐引用方式 GB/T 7714 | Liang, Shengwen,Wang, Ying,Liu, Cheng,et al. EnGN: A High-Throughput and Energy-Efficient Accelerator for Large Graph Neural Networks[J]. IEEE TRANSACTIONS ON COMPUTERS,2021,70(9):1511-1525. |
| APA | Liang, Shengwen.,Wang, Ying.,Liu, Cheng.,He, Lei.,Li, Huawei.,...&Li, Xiaowei.(2021).EnGN: A High-Throughput and Energy-Efficient Accelerator for Large Graph Neural Networks.IEEE TRANSACTIONS ON COMPUTERS,70(9),1511-1525. |
| MLA | Liang, Shengwen,et al."EnGN: A High-Throughput and Energy-Efficient Accelerator for Large Graph Neural Networks".IEEE TRANSACTIONS ON COMPUTERS 70.9(2021):1511-1525. |
入库方式： OAI收割
来源：计算技术研究所
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