{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,4]],"date-time":"2024-09-04T19:40:23Z","timestamp":1725478823972},"reference-count":28,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2023,1,31]],"date-time":"2023-01-31T00:00:00Z","timestamp":1675123200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,31]],"date-time":"2023-01-31T00:00:00Z","timestamp":1675123200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J. Comput. Sci. Technol."],"published-print":{"date-parts":[[2023,2]]},"DOI":"10.1007\/s11390-023-2875-9","type":"journal-article","created":{"date-parts":[[2023,4,4]],"date-time":"2023-04-04T11:23:14Z","timestamp":1680607394000},"page":"115-127","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["GShuttle: Optimizing Memory Access Efficiency for Graph Convolutional Neural Network Accelerators"],"prefix":"10.1007","volume":"38","author":[{"given":"Jia-Jun","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ke","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hao","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ahmed","family":"Louri","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,1,31]]},"reference":[{"key":"2875_CR1","unstructured":"Jiang W W, Luo J Y. Graph neural network for traffic forecasting: A survey. arXiv: 2101.11174, 2021. https:\/\/arxiv.org\/abs\/2101.11174, Dec. 2022."},{"key":"2875_CR2","doi-asserted-by":"publisher","unstructured":"Shi W J, Rajkumar R. Point-GNN: Graph neural network for 3D object detection in a point cloud. In Proc. the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Jun. 2020, pp.1711\u20131719. https:\/\/doi.org\/10.1109\/CVPR42600.2020.00178.","DOI":"10.1109\/CVPR42600.2020.00178"},{"key":"2875_CR3","doi-asserted-by":"publisher","unstructured":"Wee C Y, Liu C Q, Lee A, Poh J S, Ji H, Qiu A Q, The Alzheimers Disease Neuroimage Initiative. Cortical graph neural network for AD and MCI diagnosis and transfer learning across populations. NeuroImage: Clinical, 2019, 23: 101929. https:\/\/doi.org\/10.1016\/j.nicl.2019.101929.","DOI":"10.1016\/j.nicl.2019.101929"},{"issue":"1","key":"2875_CR4","doi-asserted-by":"publisher","first-page":"249","DOI":"10.1109\/TKDE.2020.2981333","volume":"34","author":"ZW Zhang","year":"2022","unstructured":"Zhang Z W, Cui P, Zhu W W. Deep learning on graphs: A survey. IEEE Trans. Knowledge and Data Engineering, 2022, 34(1): 249\u2013270. https:\/\/doi.org\/10.1109\/TKDE.2020.2981333.","journal-title":"IEEE Trans. Knowledge and Data Engineering"},{"key":"2875_CR5","doi-asserted-by":"publisher","unstructured":"Yang H X. AliGraph: A comprehensive graph neural network platform. In Proc. the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Jul. 2019, pp.3165\u20133166. https:\/\/doi.org\/10.1145\/3292500.3340404.","DOI":"10.1145\/3292500.3340404"},{"key":"2875_CR6","unstructured":"Lerer A, Wu L, Shen J, Lacroix T, Wehrstedt L, Bose A, Peysakhovich A. PyTorch-BigGraph: A large-scale graph embedding system. arXiv: 1903.12287, 2019. https:\/\/arxiv.org\/abs\/1903.12287, Dec. 2022."},{"issue":"1","key":"2875_CR7","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1109\/LCA.2020.2970395","volume":"19","author":"MY Yan","year":"2020","unstructured":"Yan M Y, Chen Z D, Deng L, Ye X C, Zhang Z M, Fan D R, Xie Y. Characterizing and understanding GCNs on GPU. IEEE Computer Architecture Letters, 2020, 19(1): 22\u201325. https:\/\/doi.org\/10.1109\/LCA.2020.2970395.","journal-title":"IEEE Computer Architecture Letters"},{"issue":"1","key":"2875_CR8","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1109\/LCA.2020.2988991","volume":"19","author":"ZH Zhang","year":"2020","unstructured":"Zhang Z H, Leng J W, Ma L X, Miao Y S, Li C, Guo M Y. Architectural implications of graph neural networks. IEEE Computer Architecture Letters, 2020, 19(1): 59\u201362. https:\/\/doi.org\/10.1109\/LCA.2020.2988991.","journal-title":"IEEE Computer Architecture Letters"},{"key":"2875_CR9","doi-asserted-by":"publisher","unstructured":"Geng T, Li A, Shi R B, Wu C S, Wang T Q, Li Y F, Haghi P, Tumeo A, Che S, Reinhardt S, Herbordt M C. AWB-GCN: A graph convolutional network accelerator with runtime workload rebalancing. In Proc. the 53rd Annual IEEE\/ACM International Symposium on Microarchitecture (MICRO), Oct. 2020, pp.922\u2013936. https:\/\/doi.org\/10.1109\/MICRO50266.2020.00079.","DOI":"10.1109\/MICRO50266.2020.00079"},{"key":"2875_CR10","doi-asserted-by":"publisher","unstructured":"Ma Y F, Cao Y, Vrudhula S, Seo J S. Optimizing loop operation and dataflow in FPGA acceleration of deep convolutional neural networks. In Proc. the 2017 ACM\/SIGDA International Symposium on Field-Programmable Gate Arrays, Feb. 2017, pp.45\u201354. https:\/\/doi.org\/10.1145\/3020078.3021736.","DOI":"10.1145\/3020078.3021736"},{"key":"2875_CR11","doi-asserted-by":"publisher","unstructured":"Yan M Y, Deng L, Hu X, Liang L, Feng, Y J, Ye X C, Zhang Z M, Fan D R, Xie Y. HyGCN: A GCN accelerator with hybrid architecture. In Proc. the 2020 IEEE International Symposium on High Performance Computer Architecture (HPCA), Feb. 2020, pp.15\u201329. https:\/\/doi.org\/10.1109\/HPCA47549.2020.00012.","DOI":"10.1109\/HPCA47549.2020.00012"},{"key":"2875_CR12","doi-asserted-by":"publisher","unstructured":"Li J J, Louri A, Karanth A, Bunescu R. GCNAX: A flexible and energy-efficient accelerator for graph convolutional neural networks. In Proc. the 2021 IEEE International Symposium on High-Performance Computer Architecture (HPCA), Mar. 2021, pp.775\u2013788. https:\/\/doi.org\/10.1109\/HPCA51647.2021.00070.","DOI":"10.1109\/HPCA51647.2021.00070"},{"issue":"7","key":"2875_CR13","doi-asserted-by":"publisher","first-page":"913","DOI":"10.1109\/TC.2010.121","volume":"60","author":"S Galal","year":"2011","unstructured":"Galal S, Horowitz M. Energy-efficient floating-point unit design. IEEE Transactions on Computers, 2011, 60(7): 913-922.","journal-title":"IEEE Transactions on Computers"},{"key":"2875_CR14","unstructured":"Kipf T N, Welling M. Semi-supervised classification with graph convolutional networks. arXiv: 1609.02907, 2016. https:\/\/arxiv.org\/abs\/1609.02907, Dec. 2022."},{"key":"2875_CR15","unstructured":"Hamilton W L, Ying R, Leskovec J. Inductive representation learning on large graphs. In Proc. the 31st International Conference on Neural Information Processing Systems, Dec. 2017, pp.1024\u20131034."},{"key":"2875_CR16","unstructured":"Xu K, Hu W H, Leskovec J, Jegelka S. How powerful are graph neural networks? arXiv: 1810.00826, 2018. https:\/\/arxiv.org\/abs\/1810.00826, Dec. 2022."},{"key":"2875_CR17","doi-asserted-by":"publisher","unstructured":"Allen J R, Kennedy K. Automatic loop interchange. In Proc. the 1984 SIGPLAN Symposium on Compiler Construction, Jun. 1984, pp.233\u2013246. https:\/\/doi.org\/10.1145\/502874.502897.","DOI":"10.1145\/502874.502897"},{"key":"2875_CR18","doi-asserted-by":"crossref","unstructured":"Zhang C, Li P, Sun G Y et al. Optimizing FPGA-based accelerator design for deep convolutional neural networks. In Proc. the 2015 ACM\/SIGDA International Symposium on Field-Programmable Gate Arrays, Feb. 2015, pp.161\u2013170. 10.1145\/2684746.2689060.","DOI":"10.1145\/2684746.2689060"},{"key":"2875_CR19","doi-asserted-by":"publisher","unstructured":"Pugh W. Uniform techniques for loop optimization. In Proc. the 5th International Conference on Supercomputing, Jun. 1991, pp.341\u2013352. https:\/\/doi.org\/10.1145\/109025.109108.","DOI":"10.1145\/109025.109108"},{"key":"2875_CR20","doi-asserted-by":"publisher","unstructured":"Pal S, Beaumont J, Park D H, Amarnath A, Feng S Y, Chakrabarti C, Kim H S, Blaauw D, Mudge T, Dreslinski R. OuterSPACE: An outer product based sparse matrix multiplication accelerator. In Proc. the 2018 IEEE International Symposium on High Performance Computer Architecture (HPCA), Feb. 2018, pp.724\u2013736. https:\/\/doi.org\/10.1109\/HPCA.2018.00067.","DOI":"10.1109\/HPCA.2018.00067"},{"key":"2875_CR21","unstructured":"Nie J. Memory-driven data-flow optimization for neural processing accelerators [Ph.D. Thesis]. Princeton University, 2020. https:\/\/www.proquest.com\/openview\/41fe23f43fd65cafaa8c2e051aed4059\/1?pq-origsite=gscholar&cbl=18750&diss=y, Jan. 2023."},{"issue":"3","key":"2875_CR22","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1609\/aimag.v29i3.2157","volume":"29","author":"P Sen","year":"2008","unstructured":"Sen P, Namata G, Bilgic M et al. Collective classification in network data. AI Magazine, 2008, 29(3): 93-106. https:\/\/doi.org\/10.1609\/aimag.v29i3.2157.","journal-title":"AI Magazine"},{"key":"2875_CR23","doi-asserted-by":"crossref","unstructured":"Carlson A, Betteridge J, Kisiel B et al. Toward an architecture for never-ending language learning. In Proc. the 34th AAAI Conference on Artificial Intelligence, July 2010, pp.1306\u20131313.","DOI":"10.1609\/aaai.v24i1.7519"},{"key":"2875_CR24","doi-asserted-by":"publisher","unstructured":"Auten A, Tomei M, Kumar R. Hardware acceleration of graph neural networks. In Proc. the 57th ACM\/IEEE Design Automation Conference (DAC), Jul. 2020. https:\/\/doi.org\/10.1109\/DAC18072.2020.9218751.","DOI":"10.1109\/DAC18072.2020.9218751"},{"issue":"9","key":"2875_CR25","doi-asserted-by":"publisher","first-page":"1511","DOI":"10.1109\/TC.2020.3014632","volume":"70","author":"SW Liang","year":"2021","unstructured":"Liang S W, Wang Y, Liu C et al. EnGN: A high-throughput and energy-efficient accelerator for large graph neural networks. IEEE Trans. Computers, 2021, 70(9): 1511\u20131525. https:\/\/doi.org\/10.1109\/TC.2020.3014632.","journal-title":"IEEE Trans. Computers"},{"key":"2875_CR26","unstructured":"Kiningham K, Re C, Levis P. GRIP: A graph neural network accelerator architecture. arXiv: 2007.13828, 2020. https:\/\/arxiv.org\/abs\/2007.13828v1, Dec. 2022."},{"key":"2875_CR27","doi-asserted-by":"publisher","unstructured":"Zeng H Q, Prasanna V. GraphACT: Accelerating GCN training on CPU-FPGA heterogeneous platforms. In Proc. the 2020 ACM\/SIGDA International Symposium on Field-Programmable Gate Arrays, Feb. 2020, pp.255\u2013265. https:\/\/doi.org\/10.1145\/3373087.3375312.","DOI":"10.1145\/3373087.3375312"},{"key":"2875_CR28","unstructured":"Shi F, Jin A Y, Zhu S C. VersaGNN: A versatile accelerator for graph neural networks. arXiv: 2105.01280, 2021. https:\/\/arxiv.org\/abs\/2105.01280, Dec. 2022."}],"container-title":["Journal of Computer Science and Technology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11390-023-2875-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11390-023-2875-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11390-023-2875-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,4,4]],"date-time":"2023-04-04T12:05:08Z","timestamp":1680609908000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11390-023-2875-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,31]]},"references-count":28,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2023,2]]}},"alternative-id":["2875"],"URL":"https:\/\/doi.org\/10.1007\/s11390-023-2875-9","relation":{},"ISSN":["1000-9000","1860-4749"],"issn-type":[{"value":"1000-9000","type":"print"},{"value":"1860-4749","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1,31]]},"assertion":[{"value":"29 September 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 January 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 January 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}