{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,27]],"date-time":"2026-06-27T19:43:37Z","timestamp":1782589417030,"version":"3.54.5"},"reference-count":53,"publisher":"IEEE","license":[{"start":{"date-parts":[[2023,2,1]],"date-time":"2023-02-01T00:00:00Z","timestamp":1675209600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,2,1]],"date-time":"2023-02-01T00:00:00Z","timestamp":1675209600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,2]]},"DOI":"10.1109\/hpca56546.2023.10070983","type":"proceedings-article","created":{"date-parts":[[2023,3,24]],"date-time":"2023-03-24T17:42:55Z","timestamp":1679679775000},"page":"42-55","source":"Crossref","is-referenced-by-count":53,"title":["GROW: A Row-Stationary Sparse-Dense GEMM Accelerator for Memory-Efficient Graph Convolutional Neural Networks"],"prefix":"10.1109","author":[{"given":"Ranggi","family":"Hwang","sequence":"first","affiliation":[{"name":"KAIST,School of Electrical Engineering"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Minhoo","family":"Kang","sequence":"additional","affiliation":[{"name":"KAIST,School of Electrical Engineering"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiwon","family":"Lee","sequence":"additional","affiliation":[{"name":"KAIST,School of Electrical Engineering"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dongyun","family":"Kam","sequence":"additional","affiliation":[{"name":"POSTECH,Department of Electrical Engineering"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Youngjoo","family":"Lee","sequence":"additional","affiliation":[{"name":"POSTECH,Department of Electrical Engineering"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Minsoo","family":"Rhu","sequence":"additional","affiliation":[{"name":"KAIST,School of Electrical Engineering"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1145\/3477141"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/DAC18072.2020.9218751"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/ISSCC.2016.7418007"},{"key":"ref4","first-page":"1114","article-title":"Learning Steady-States of Iterative Algorithms over Graphs","author":"Dai","year":"2018","journal-title":"ICML"},{"key":"ref5","article-title":"MolGAN: An Implicit Generative Model for Small Molecular Graphs","author":"De Cao","year":"2018"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1145\/1081870.1081948"},{"key":"ref7","article-title":"Convolutional Networks on Graphs for Learning Molecular Fingerprints","volume-title":"Proceedings of the International Conference on Neural Information Processing Systems (NIPS)","author":"Duvenaud"},{"key":"ref8","article-title":"Fast Graph Representation Learning with PyTorch Geometric","author":"Fey","year":"2019"},{"key":"ref9","article-title":"Protein Interface Prediction Using Graph Convolutional Networks","volume-title":"Proceedings of the International Conference on Neural Information Processing Systems (NIPS)","author":"Fout"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/MICRO50266.2020.00079"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1145\/355791.355796"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/hpca47549.2020.00035"},{"key":"ref13","article-title":"Inductive Representation Learning on Large Graphs","volume-title":"Proceedings of the International Conference on Neural Information Processing Systems (NIPS)","author":"Hamilton"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1145\/3352460.3358275"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/ISSCC.2014.6757323"},{"key":"ref16","article-title":"CACTI: An Integrated Cache and Memory Access Time, Cycle Time, Area, Leakage, and Dynamic Power Model","author":"Labs","year":"2016"},{"key":"ref17","article-title":"Open Graph Benchmark: Datasets for Machine Learning on Graphs","author":"Hu","year":"2020"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/SC41405.2020.00076"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/ISCA45697.2020.00083"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1137\/s1064827595287997"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/LCA.2020.3042805"},{"key":"ref22","article-title":"GReTA: Hardware Optimized Graph Processing for GNNs","volume-title":"Proceedings of the Workshop on Resource-Constrained Machine Learning (ReCoML)","author":"Kiningham"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/tc.2022.3197083"},{"key":"ref24","article-title":"Semi-Supervised Classification with Graph Convolutional Networks","volume-title":"Proceedings of the International Conference on Learning Representations (ICLR)","author":"Kipf"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1145\/3352460.3358284"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/HPCA51647.2021.00029"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1145\/3470496.3527386"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1145\/3470496.3527391"},{"key":"ref29","article-title":"Pytorch-BigGraph: A Large-Scale Graph Embedding System","author":"Lerer","year":"2019"},{"key":"ref30","article-title":"SNAP Datasets: Stanford Large Network Dataset Collection","author":"Leskovec","year":"2014"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/HPCA51647.2021.00070"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/TC.2020.3014632"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1145\/3531437.3539717"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/HPCA.2018.00067"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1145\/3079856.3080254"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1145\/3466752.3480080"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/HPCA.2018.00017"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/TCAD.2021.3052138"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/MICRO50266.2020.00068"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/HPCA47549.2020.00062"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.14711\/thesis-991012757468803412"},{"key":"ref42","article-title":"GNNAdvisor: An Adaptive and Efficient Runtime System for GNN Acceleration on GPUs","volume-title":"Proceedings of the International Symposium on Operating Systems Design and Implementation (OSDI)","author":"Wang"},{"key":"ref43","article-title":"How Powerful are Graph Neural Networks?","author":"Xu","year":"2018"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/HPCA47549.2020.00012"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3340404"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.5555\/3327345.3327389"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1145\/3373087.3375312"},{"key":"ref48","article-title":"Graph-saint: Graph Sampling Based Inductive Learning Method","volume-title":"Proceedings of the International Conference on Learning Representations (ICLR)","author":"Zeng"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/FCCM51124.2021.00012"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/ASAP49362.2020.00019"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1145\/3445814.3446702"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1145\/3174243.3174245"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/HPCA47549.2020.00030"}],"event":{"name":"2023 IEEE International Symposium on High-Performance Computer Architecture (HPCA)","location":"Montreal, QC, Canada","start":{"date-parts":[[2023,2,25]]},"end":{"date-parts":[[2023,3,1]]}},"container-title":["2023 IEEE International Symposium on High-Performance Computer Architecture (HPCA)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/10070856\/10070923\/10070983.pdf?arnumber=10070983","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,13]],"date-time":"2024-02-13T13:12:47Z","timestamp":1707829967000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10070983\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2]]},"references-count":53,"URL":"https:\/\/doi.org\/10.1109\/hpca56546.2023.10070983","relation":{},"subject":[],"published":{"date-parts":[[2023,2]]}}}