{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:16:45Z","timestamp":1750220205991,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":24,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,8,29]],"date-time":"2022-08-29T00:00:00Z","timestamp":1661731200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61902063"],"award-info":[{"award-number":["61902063"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"State Key Laboratory of Mathematical Engineering and Advanced Computing","award":["2020A04"],"award-info":[{"award-number":["2020A04"]}]},{"name":"Provincial Natural Science Foundation of Jiangsu, China","award":["BK20190342"],"award-info":[{"award-number":["BK20190342"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,8,29]]},"DOI":"10.1145\/3547276.3548521","type":"proceedings-article","created":{"date-parts":[[2023,1,15]],"date-time":"2023-01-15T00:56:17Z","timestamp":1673744177000},"page":"1-7","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["A Software\/Hardware Co-design Local Irregular Sparsity Method for Accelerating CNNs on FPGA"],"prefix":"10.1145","author":[{"given":"Jiangwei","family":"Shang","sequence":"first","affiliation":[{"name":"Harbin Institute of Technology, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhan","family":"Zhang","sequence":"additional","affiliation":[{"name":"Harbin Institute of Technology, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chuanyou","family":"Li","sequence":"additional","affiliation":[{"name":"Southeast University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kun","family":"Zhang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Mathematical Engineering and Advanced Computing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lei","family":"Qian","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Mathematical Engineering and Advanced Computing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongwei","family":"Liu","sequence":"additional","affiliation":[{"name":"Harbin Institute of Technology, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2023,1,13]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Very deep convolutional networks for large-scale image recognition","author":"Simonyan K.","year":"2014","unstructured":"K. Simonyan and A. Zisserman, \u201cVery deep convolutional networks for large-scale image recognition,\u201d 2014, arXiv:1409.1556. [Online]. Available: http:\/\/arxiv.org\/abs\/1409.1556"},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.91"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1512.03385"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCAD.2017.2705069"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/3174243.3174265"},{"key":"e_1_3_2_1_7_1","volume-title":"Advances in Neural Information Processing Systems","author":"Han S.","year":"2015","unstructured":"S. Han, J. Pool, J. Tran, and W. Dally, \u201cLearning both weights and connections for efficient neural network,\u201d in Advances in Neural Information Processing Systems, 2015."},{"key":"e_1_3_2_1_8_1","volume-title":"Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding","author":"Han S.","year":"2015","unstructured":"S. Han, H. Mao, and W. J. Dally, \u201cDeep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding,\u201d arXiv preprint arXiv:1510.00149, 2015."},{"key":"e_1_3_2_1_9_1","volume-title":"Proc. IEEE Conf. Comput Vis. Pattern Recongnit (CVPR)","author":"Liu B.","year":"2015","unstructured":"B. Liu, M. Wang, H. Foroosh, M. Tappen, and M. Pensky. \u201cSparse Convolutional Neural Networks,\u201d in Proc. IEEE Conf. Comput Vis. Pattern Recongnit (CVPR) 2015."},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/3079856.3080254"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCAD.2020.3023903"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/MICRO.2018.00011"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/3123939.3124552"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/HiPC.2019.00033"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICVGIP.2008.47"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/FCCM.2019.00013"},{"key":"e_1_3_2_1_17_1","first-page":"01955","article-title":"An efficient hardware accelerator for structured sparse convolutional neural networks on FPGAs","volume":"2001","author":"Zhu C.","year":"2020","unstructured":"C. Zhu, K. Huang, S. Yang, Z. Zhu, H. Zhang, and H. Shen, \u2018\u2018An efficient hardware accelerator for structured sparse convolutional neural networks on FPGAs,\u2019\u2019 CoRR, vol. abs\/2001.01955, 2020. [Online]. Available: http:\/\/arxiv.org\/abs\/2001.01955","journal-title":"CoRR"},{"key":"e_1_3_2_1_18_1","first-page":"2074","article-title":"Learning structured sparsity in deep neural networks","author":"Wen W.","year":"2016","unstructured":"W. Wen, C. Wu, Y. Wang, Y. Chen, and H. Li, \u201cLearning structured sparsity in deep neural networks,\u201d in Proc. Adv. Neural Inf. Process. Syst., 2016, pp. 2074\u20132082.","journal-title":"Proc. Adv. Neural Inf. Process. Syst."},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.155"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1109\/WACV45572.2020.9093331"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.541"},{"key":"e_1_3_2_1_22_1","first-page":"3131","volume-title":"Advances in neural information processing systems (NeurIPS)","author":"Courbariaux M.","year":"2015","unstructured":"M. Courbariaux, Y. Bengio, J.-P. David, Binaryconnect: Training deep neural networks with binary weights during propagations, in Advances in neural information processing systems (NeurIPS), 2015, pp. 3123\u2013 3131."},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/FPL50879.2020.00055"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"}],"event":{"name":"ICPP '22: 51st International Conference on Parallel Processing","acronym":"ICPP '22","location":"Bordeaux France"},"container-title":["Workshop Proceedings of the 51st International Conference on Parallel Processing"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3547276.3548521","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3547276.3548521","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T19:02:56Z","timestamp":1750186976000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3547276.3548521"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,29]]},"references-count":24,"alternative-id":["10.1145\/3547276.3548521","10.1145\/3547276"],"URL":"https:\/\/doi.org\/10.1145\/3547276.3548521","relation":{},"subject":[],"published":{"date-parts":[[2022,8,29]]},"assertion":[{"value":"2023-01-13","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}