{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,27]],"date-time":"2025-06-27T15:10:08Z","timestamp":1751037008091,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":19,"publisher":"ACM","funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["92464301"],"award-info":[{"award-number":["92464301"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,6,30]]},"DOI":"10.1145\/3716368.3735211","type":"proceedings-article","created":{"date-parts":[[2025,6,27]],"date-time":"2025-06-27T13:58:23Z","timestamp":1751032703000},"page":"798-804","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["D-GCN: A Dynamic Pruning Accelerator for Deep Graph Convolutional Networks with Hybrid Dataflow"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9003-8966","authenticated-orcid":false,"given":"Shun","family":"Li","sequence":"first","affiliation":[{"name":"School of Integrated Circuits, Southeast University, Nanjing, Jiangsu, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-8421-1242","authenticated-orcid":false,"given":"Hao","family":"Zhou","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0539-8885","authenticated-orcid":false,"given":"Enhao","family":"Tang","sequence":"additional","affiliation":[{"name":"School of Electronic Science and Engineering, Nanjing University, Nanjing, Jiangsu, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-0911-0666","authenticated-orcid":false,"given":"Yang","family":"Liu","sequence":"additional","affiliation":[{"name":"State Key Lab of ASIC &amp; System, Fudan University, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5493-7411","authenticated-orcid":false,"given":"Shidi","family":"Tang","sequence":"additional","affiliation":[{"name":"School of Integrated Circuits, Southeast University, Nanjing, Jiangsu, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8866-7189","authenticated-orcid":false,"given":"Ming","family":"Ling","sequence":"additional","affiliation":[{"name":"School of Integrated Circuits, Southeast University, Nanjing, Jiangsu, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,6,29]]},"reference":[{"key":"e_1_3_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.5555\/3524938.3525099"},{"key":"e_1_3_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/ISCAS46773.2023.10181734"},{"key":"e_1_3_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.1109\/FPL60245.2023.00054"},{"key":"e_1_3_3_1_5_2","doi-asserted-by":"publisher","DOI":"10.1109\/MICRO50266.2020.00079"},{"key":"e_1_3_3_1_6_2","doi-asserted-by":"crossref","unstructured":"Fred\u00a0G Gustavson. 1978. Two fast algorithms for sparse matrices: Multiplication and permuted transposition. ACM Transactions on Mathematical Software (TOMS) 4 3 (1978) 250\u2013269.","DOI":"10.1145\/355791.355796"},{"key":"e_1_3_3_1_7_2","doi-asserted-by":"publisher","DOI":"10.1109\/HPCA51647.2021.00070"},{"key":"e_1_3_3_1_8_2","doi-asserted-by":"crossref","unstructured":"Jingyu Liu Shi Chen and Li Shen. 2025. A comprehensive survey on graph neural network accelerators. Frontiers of Computer Science 19 2 (2025) 192104.","DOI":"10.1007\/s11704-023-3307-2"},{"key":"e_1_3_3_1_9_2","volume-title":"NVIDIA GeForce RTX 3090 Whitepaper","author":"Corporation NVIDIA","year":"2020","unstructured":"NVIDIA Corporation. 2020. NVIDIA GeForce RTX 3090 Whitepaper. White Paper. NVIDIA Corporation. https:\/\/www.nvidia.com\/en-us\/geforce\/graphics-cards\/30-series\/rtx-3090\/ [Online]."},{"key":"e_1_3_3_1_10_2","unstructured":"Charles\u00a0Ruizhongtai Qi Li Yi Hao Su and Leonidas\u00a0J Guibas. 2017. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017)."},{"key":"e_1_3_3_1_11_2","doi-asserted-by":"crossref","unstructured":"Guanqiu Qin Nankai Lin Menglan Shen Qifeng Bai Dong Zhou and Aimin Yang. 2024. Global information enhancement and subgraph-level weakly contrastive learning for lightweight weakly supervised document-level event extraction. Expert Systems with Applications 240 (2024) 122516.","DOI":"10.1016\/j.eswa.2023.122516"},{"key":"e_1_3_3_1_12_2","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3220077"},{"key":"e_1_3_3_1_13_2","doi-asserted-by":"publisher","DOI":"10.1145\/3637528.3671766"},{"key":"e_1_3_3_1_14_2","first-page":"11592","volume-title":"International Conference on Machine Learning","author":"Xu Keyulu","year":"2021","unstructured":"Keyulu Xu, Mozhi Zhang, Stefanie Jegelka, and Kenji Kawaguchi. 2021. Optimization of graph neural networks: Implicit acceleration by skip connections and more depth. In International Conference on Machine Learning. PMLR, 11592\u201311602."},{"key":"e_1_3_3_1_15_2","doi-asserted-by":"publisher","DOI":"10.1109\/HPCA47549.2020.00012"},{"key":"e_1_3_3_1_16_2","first-page":"40","volume-title":"International conference on machine learning","author":"Yang Zhilin","year":"2016","unstructured":"Zhilin Yang, William Cohen, and Ruslan Salakhudinov. 2016. Revisiting semi-supervised learning with graph embeddings. In International conference on machine learning. PMLR, 40\u201348."},{"key":"e_1_3_3_1_17_2","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219890"},{"key":"e_1_3_3_1_18_2","doi-asserted-by":"crossref","unstructured":"Qianmu Yuan Jianwen Chen Huiying Zhao Yaoqi Zhou and Yuedong Yang. 2022. Structure-aware protein\u2013protein interaction site prediction using deep graph convolutional network. Bioinformatics 38 1 (2022) 125\u2013132.","DOI":"10.1093\/bioinformatics\/btab643"},{"key":"e_1_3_3_1_19_2","unstructured":"Muhan Zhang and Yixin Chen. 2018. Link prediction based on graph neural networks. Advances in neural information processing systems 31 (2018)."},{"key":"e_1_3_3_1_20_2","unstructured":"Xiaofeng Zou Cen Chen Luochuan Zhang Shengyang Li Joey\u00a0Tianyi Zhou Wei Wei and Kenli Li. 2024. Efficient Message Passing Algorithm and Architecture Co-Design for Graph Neural Networks. IEEE Transactions on Emerging Topics in Computational Intelligence (2024)."}],"event":{"name":"GLSVLSI '25: Great Lakes Symposium on VLSI 2025","sponsor":["SIGDA ACM Special Interest Group on Design Automation"],"location":"New Orleans LA USA","acronym":"GLSVLSI '25"},"container-title":["Proceedings of the Great Lakes Symposium on VLSI 2025"],"original-title":[],"deposited":{"date-parts":[[2025,6,27]],"date-time":"2025-06-27T14:37:38Z","timestamp":1751035058000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3716368.3735211"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,29]]},"references-count":19,"alternative-id":["10.1145\/3716368.3735211","10.1145\/3716368"],"URL":"https:\/\/doi.org\/10.1145\/3716368.3735211","relation":{},"subject":[],"published":{"date-parts":[[2025,6,29]]},"assertion":[{"value":"2025-06-29","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}