{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T05:10:13Z","timestamp":1782450613810,"version":"3.54.5"},"reference-count":20,"publisher":"IEEE","license":[{"start":{"date-parts":[[2023,5,21]],"date-time":"2023-05-21T00:00:00Z","timestamp":1684627200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,5,21]],"date-time":"2023-05-21T00:00:00Z","timestamp":1684627200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,5,21]]},"DOI":"10.1109\/iscas46773.2023.10182227","type":"proceedings-article","created":{"date-parts":[[2023,7,21]],"date-time":"2023-07-21T17:19:44Z","timestamp":1689959984000},"page":"1-5","source":"Crossref","is-referenced-by-count":1,"title":["SaGNN: a Sample-based GNN Training and Inference Hardware Accelerator"],"prefix":"10.1109","author":[{"given":"Haoyang","family":"Wang","sequence":"first","affiliation":[{"name":"Northwestern Polytechnical University,Xi'an,China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shengbing","family":"Zhang","sequence":"additional","affiliation":[{"name":"Northwestern Polytechnical University,Xi'an,China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kaijie","family":"Feng","sequence":"additional","affiliation":[{"name":"Northwestern Polytechnical University,Xi'an,China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Miao","family":"Wang","sequence":"additional","affiliation":[{"name":"Northwestern Polytechnical University,Xi'an,China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhao","family":"Yang","sequence":"additional","affiliation":[{"name":"Northwestern Polytechnical University,Xi'an,China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1145\/3419111.3421281"},{"key":"ref2","first-page":"99","article-title":"Firmament: Fast, centralized cluster scheduling at scale","volume-title":"Proceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation","author":"Gog"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1145\/3469379.3469387"},{"key":"ref4","article-title":"Fast graph representation learning with pytorch geometric","author":"Fey","year":"2019","journal-title":"arXiv preprint"},{"key":"ref5","volume-title":"Deep graph library: Towards efficient and scalable deep learning on graphs","author":"Wang","year":"2019"},{"key":"ref6","article-title":"Semi-supervised classification with graph convolutional networks","volume-title":"International Conference on Learning Repre-sentations","author":"Kipf"},{"key":"ref7","first-page":"1024","article-title":"Inductive representation learning on large graphs","author":"Hamilton","year":"2017","journal-title":"Advances in neural information processing systems"},{"key":"ref8","article-title":"Residual gated graph convnets","author":"Bresson","year":"2017","journal-title":"arXiv preprint"},{"key":"ref9","first-page":"5165","article-title":"Link prediction based on graph neural networks","volume":"31","author":"Zhang","year":"2018","journal-title":"Advances in Neural Information Processing Systems"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/HPCA47549.2020.00012"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/MICRO50266.2020.00079"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/DAC18072.2020.9218751"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.2978386"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/LCA.2015.2414456"},{"key":"ref15","article-title":"Redundancy-Free Computation Graphs for Graph Neural Networks","author":"Jia","year":"2019","journal-title":"arXiv"},{"key":"ref16","first-page":"17","article-title":"Pow-erGraph: Distributed Graph-Parallel Computation on Natural Graphs","volume-title":"Proceedings of the Symposium on Operating Systems Design and Implementation (OSDI)","author":"Gonzalez"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.14711\/thesis-991012757468803412"},{"key":"ref18","article-title":"Few-Shot Learning with Graph Neural Networks","volume-title":"Proceedings of the 6th International Conference on Learning Representations (ICLR18)","author":"Satorras"},{"key":"ref19","first-page":"941","article-title":"Stochastic Training of Graph Convolutional Networks with Variance Reduction","volume-title":"Proceedings of the 35th International Conference on Machine Learning (ICML18)","author":"Chen"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/jas.2021.1004311"}],"event":{"name":"2023 IEEE International Symposium on Circuits and Systems (ISCAS)","location":"Monterey, CA, USA","start":{"date-parts":[[2023,5,21]]},"end":{"date-parts":[[2023,5,25]]}},"container-title":["2023 IEEE International Symposium on Circuits and Systems (ISCAS)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/10181241\/10181318\/10182227.pdf?arnumber=10182227","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,4,13]],"date-time":"2024-04-13T04:43:26Z","timestamp":1712983406000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10182227\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,21]]},"references-count":20,"URL":"https:\/\/doi.org\/10.1109\/iscas46773.2023.10182227","relation":{},"subject":[],"published":{"date-parts":[[2023,5,21]]}}}