{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T19:49:10Z","timestamp":1776887350211,"version":"3.51.2"},"reference-count":64,"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\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,2]]},"DOI":"10.1109\/hpca56546.2023.10071015","type":"proceedings-article","created":{"date-parts":[[2023,3,24]],"date-time":"2023-03-24T17:42:55Z","timestamp":1679679775000},"page":"1099-1112","source":"Crossref","is-referenced-by-count":86,"title":["FlowGNN: A Dataflow Architecture for Real-Time Workload-Agnostic Graph Neural Network Inference"],"prefix":"10.1109","author":[{"given":"Rishov","family":"Sarkar","sequence":"first","affiliation":[{"name":"Georgia Institute of Technology,School of Electrical and Computer Engineering"}]},{"given":"Stefan","family":"Abi-Karam","sequence":"additional","affiliation":[{"name":"Georgia Institute of Technology,School of Electrical and Computer Engineering"}]},{"given":"Yuqi","family":"He","sequence":"additional","affiliation":[{"name":"Georgia Institute of Technology,School of Electrical and Computer Engineering"}]},{"given":"Lakshmi","family":"Sathidevi","sequence":"additional","affiliation":[{"name":"Georgia Institute of Technology,School of Electrical and Computer Engineering"}]},{"given":"Cong","family":"Hao","sequence":"additional","affiliation":[{"name":"Georgia Institute of Technology,School of Electrical and Computer Engineering"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1093\/nar\/gky1131"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1093\/nar\/gkx1037"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1039\/C7SC02664A"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-021-00418-8"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00178"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1088\/2632-2153\/abbf9a"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.3389\/fdata.2020.598927"},{"key":"ref8","author":"Cerminara","year":"2020","journal-title":"Distance-weighted graph neural networks on FPGAs for real-time particle reconstruction at the Large Hadron Collider"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1140\/epjp\/s13360-020-00497-3"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevD.101.056019"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.3389\/fdata.2022.828666"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/ASAP49362.2020.00019"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/DAC18074.2021.9586129"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/DAC18072.2020.9218751"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/MICRO50266.2020.00079"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/FCCM51124.2021.00012"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1145\/3466752.3480113"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403142"},{"key":"ref19","article-title":"GNNAdvisor: An efficient runtime system for GNN acceleration on GPUs","author":"Wang","year":"2020"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/TCAD.2021.3079142"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/HPCA47549.2020.00012"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/MICRO50266.2020.00079"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/HPCA47549.2020.00012"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/HPCA53966.2022.00041"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/HPCA53966.2022.00039"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/HPCA53966.2022.00079"},{"key":"ref27","article-title":"Semi-supervised classification with graph convolutional networks","author":"Kipf","year":"2016","journal-title":"ICLR"},{"key":"ref28","article-title":"How powerful are graph neural networks?","author":"Xu","year":"2019","journal-title":"ICLR"},{"key":"ref29","author":"Hu","year":"2019","journal-title":"Strategies for pre-training graph neural networks"},{"key":"ref30","article-title":"Graph attention networks","author":"Veli\u010dkovi\u0107","year":"2017"},{"key":"ref31","article-title":"Principal neighbourhood aggregation for graph nets","volume-title":"NeurIPS","author":"Corso"},{"key":"ref32","first-page":"748","article-title":"Directional graph networks","volume-title":"International Conference on Machine Learning","author":"Beani"},{"key":"ref33","article-title":"Neural message passing for quantum chemistry","author":"Gilmer","year":"2017","journal-title":"ICML"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1145\/3431920.3439290"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1145\/3477141"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/TC.2020.3014632"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/HPCA51647.2021.00070"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/TCAD.2021.3079142"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1145\/3373087.3375312"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00303"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00943"},{"key":"ref42","article-title":"Do we need anisotropic graph neural networks?","author":"Tailor","year":"2021","journal-title":"International Conference on Learning Representations"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1145\/3373087.3375312"},{"key":"ref44","article-title":"Versagnn: a versatile accelerator for graph neural networks","author":"Shi","year":"2021"},{"key":"ref45","author":"Veli\u010dkovi\u0107","year":"2022","journal-title":"Message passing all the way up"},{"key":"ref46","article-title":"Simplifying graph convolutional networks","author":"Wu","year":"2019","journal-title":"ICML"},{"key":"ref47","first-page":"1025","article-title":"Inductive representation learning on large graphs","volume-title":"Proceedings of the 31st International Conference on Neural Information Processing Systems","author":"Hamilton"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/DAC18072.2020.9218757"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/tpami.2021.3081010"},{"key":"ref50","article-title":"Graph warp module: an auxiliary module for boosting the power of graph neural networks","author":"Ishiguro","year":"2019"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1109\/CDS52072.2021.00101"},{"key":"ref52","article-title":"Graph classification via deep learning with virtual nodes","author":"Pham","year":"2017"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1088\/1742-5468\/ac3ae4"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref55","volume-title":"GNN models from Open Graph Benchmark"},{"key":"ref56","first-page":"22118","article-title":"Open graph benchmark: Datasets for machine learning on graphs","volume-title":"Advances in Neural Information Processing Systems","volume":"33","author":"Hu","year":"2020"},{"key":"ref57","author":"Kasieczka","year":"2019","journal-title":"Top quark tagging reference dataset"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1142\/9789811234026_0012"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1109\/CICC53496.2022.9772832"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1609\/aimag.v29i3.2157"},{"key":"ref61","volume-title":"PyTorch Geometric"},{"key":"ref62","first-page":"7","year":"2020","journal-title":"The challenge of batch size 1: Groq adds responsiveness to inference performance"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1109\/ICRC.2019.8914707"},{"key":"ref64","author":"Sarkar","year":"2022","journal-title":"Sharc-lab\/FlowGNN: V1.0.0. Zenodo"}],"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\/10071015.pdf?arnumber=10071015","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,13]],"date-time":"2024-02-13T13:27:16Z","timestamp":1707830836000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10071015\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2]]},"references-count":64,"URL":"https:\/\/doi.org\/10.1109\/hpca56546.2023.10071015","relation":{},"subject":[],"published":{"date-parts":[[2023,2]]}}}