乐胖代购免代理版

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Archit. Code Optim."],"published-print":{"date-parts":[[2023,12,31]]},"abstract":"\n Dynamic Graph Neural Network<\/jats:italic>\n (DGNN) has recently attracted a significant amount of research attention from various domains, because most real-world graphs are inherently dynamic. Despite many research efforts, for DGNN, existing hardware\/software solutions still suffer significantly from redundant computation and memory access overhead, because they need to irregularly access and recompute all graph data of each graph snapshot. To address these issues, we propose an efficient redundancy-aware accelerator,\n RACE<\/jats:italic>\n , which enables energy-efficient execution of DGNN models. Specifically, we propose a\n redundancy-aware incremental execution approach<\/jats:italic>\n into the accelerator design for DGNN to instantly achieve the output features of the latest graph snapshot by correctly and incrementally refining the output features of the previous graph snapshot and also enable regular accesses of vertices\u2019 input features. Through traversing the graph on the fly, RACE identifies the vertices that are not affected by graph updates between successive snapshots to reuse these vertices\u2019 states (i.e., their output features) of the previous snapshot for the processing of the latest snapshot. The vertices affected by graph updates are also tracked to incrementally recompute their new states using their neighbors\u2019 input features of the latest snapshot for correctness. In this way, the processing and accessing of many graph data that are not affected by graph updates can be correctly eliminated, enabling smaller redundant computation and memory access overhead. Besides, the input features, which are accessed more frequently, are dynamically identified according to graph topology and are preferentially resident in the on-chip memory for less off-chip communications. Experimental results show that RACE achieves on average 1139\u00d7 and 84.7\u00d7 speedups for DGNN inference, with average 2242\u00d7 and 234.2\u00d7 energy savings, in comparison with the state-of-the-art software DGNN running on Intel Xeon CPU and NVIDIA A100 GPU, respectively. Moreover, for DGNN inference, RACE obtains on average 13.1\u00d7, 11.7\u00d7, 10.4\u00d7, and 7.9\u00d7 speedup and 14.8\u00d7, 12.9\u00d7, 11.5\u00d7, and 8.9\u00d7 energy savings over the state-of-the-art Graph Neural Network accelerators, i.e., AWB-GCN, GCNAX, ReGNN, and I-GCN, respectively.\n <\/jats:p>","DOI":"10.1145\/3617685","type":"journal-article","created":{"date-parts":[[2023,8,30]],"date-time":"2023-08-30T09:52:01Z","timestamp":1693389121000},"page":"1-26","update-policy":"http:\/\/dx.doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["RACE: An Efficient Redundancy-aware Accelerator for Dynamic Graph Neural Network"],"prefix":"10.1145","volume":"20","author":[{"ORCID":"http:\/\/orcid.org\/0000-0002-6559-6111","authenticated-orcid":false,"given":"Hui","family":"Yu","sequence":"first","affiliation":[{"name":"National Engineering Research Center for Big Data Technology and System, Service Computing Technology and System Lab, Cluster and Grid Computing Lab, School of Computer Science and Technology, Huazhong University of Science and Technology, China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0003-0718-8045","authenticated-orcid":false,"given":"Yu","family":"Zhang","sequence":"additional","affiliation":[{"name":"National Engineering Research Center for Big Data Technology and System, Service Computing Technology and System Lab, Cluster and Grid Computing Lab, School of Computer Science and Technology, Huazhong University of Science and Technology, China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0003-4217-7886","authenticated-orcid":false,"given":"Jin","family":"Zhao","sequence":"additional","affiliation":[{"name":"National Engineering Research Center for Big Data Technology and System, Service Computing Technology and System Lab, Cluster and Grid Computing Lab, School of Computer Science and Technology, Huazhong University of Science and Technology, China"}]},{"ORCID":"http:\/\/orcid.org\/0009-0008-9610-9444","authenticated-orcid":false,"given":"Yujian","family":"Liao","sequence":"additional","affiliation":[{"name":"National Engineering Research Center for Big Data Technology and System, Service Computing Technology and System Lab, Cluster and Grid Computing Lab, School of Computer Science and Technology, Huazhong University of Science and Technology, China"}]},{"ORCID":"http:\/\/orcid.org\/0009-0006-5088-8249","authenticated-orcid":false,"given":"Zhiying","family":"Huang","sequence":"additional","affiliation":[{"name":"National Engineering Research Center for Big Data Technology and System, Service Computing Technology and System Lab, Cluster and Grid Computing Lab, School of Computer Science and Technology, Huazhong University of Science and Technology, China"}]},{"ORCID":"http:\/\/orcid.org\/0009-0000-4374-0293","authenticated-orcid":false,"given":"Donghao","family":"He","sequence":"additional","affiliation":[{"name":"National Engineering Research Center for Big Data Technology and System, Service Computing Technology and System Lab, Cluster and Grid Computing Lab, School of Computer Science and Technology, Huazhong University of Science and Technology, China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-6525-9334","authenticated-orcid":false,"given":"Lin","family":"Gu","sequence":"additional","affiliation":[{"name":"National Engineering Research Center for Big Data Technology and System, Service Computing Technology and System Lab, Cluster and Grid Computing Lab, School of Computer Science and Technology, Huazhong University of Science and Technology, China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-3934-7605","authenticated-orcid":false,"given":"Hai","family":"Jin","sequence":"additional","affiliation":[{"name":"National Engineering Research Center for Big Data Technology and System, Service Computing Technology and System Lab, Cluster and Grid Computing Lab, School of Computer Science and Technology, Huazhong University of Science and Technology, China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0001-6302-813X","authenticated-orcid":false,"given":"Xiaofei","family":"Liao","sequence":"additional","affiliation":[{"name":"National Engineering Research Center for Big Data Technology and System, Service Computing Technology and System Lab, Cluster and Grid Computing Lab, School of Computer Science and Technology, Huazhong University of Science and Technology, China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0003-4290-1408","authenticated-orcid":false,"given":"Haikun","family":"Liu","sequence":"additional","affiliation":[{"name":"National Engineering Research Center for Big Data Technology and System, Service Computing Technology and System Lab, Cluster and Grid Computing Lab, School of Computer Science and Technology, Huazhong University of Science and Technology, China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0001-8618-4581","authenticated-orcid":false,"given":"Bingsheng","family":"He","sequence":"additional","affiliation":[{"name":"National University of Singapore, Singapore"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-1876-6931","authenticated-orcid":false,"given":"Jianhui","family":"Yue","sequence":"additional","affiliation":[{"name":"Michigan Technological University, America"}]}],"member":"320","published-online":{"date-parts":[[2023,12,14]]},"reference":[{"key":"e_1_3_2_2_2","unstructured":"2022. 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