{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,7]],"date-time":"2026-01-07T18:39:20Z","timestamp":1767811160593,"version":"3.49.0"},"publisher-location":"New York, NY, USA","reference-count":23,"publisher":"ACM","license":[{"start":{"date-parts":[[2023,1,16]],"date-time":"2023-01-16T00:00:00Z","timestamp":1673827200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["62172387"],"award-info":[{"award-number":["62172387"]}]},{"name":"Youth Innovation Promotion Association CAS","award":["2021098"],"award-info":[{"award-number":["2021098"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,1,16]]},"DOI":"10.1145\/3566097.3567868","type":"proceedings-article","created":{"date-parts":[[2023,1,31]],"date-time":"2023-01-31T18:40:49Z","timestamp":1675190449000},"page":"739-744","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Mortar"],"prefix":"10.1145","author":[{"given":"Yunhung","family":"Gao","sequence":"first","affiliation":[{"name":"Peking University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongyan","family":"Li","sequence":"additional","affiliation":[{"name":"University of Chinese Academy of Sciences, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kevin","family":"Zhang","sequence":"additional","affiliation":[{"name":"Peking University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xueru","family":"Yu","sequence":"additional","affiliation":[{"name":"Shanghai Integrated Circuits R&amp;D Center Co. Ltd, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hang","family":"Lu","sequence":"additional","affiliation":[{"name":"University of Chinese Academy of Sciences, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2023,1,31]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"In-Datacenter Performance Analysis of a Tensor ProcessinUnit,\" in ISCA","author":"Jouppi N. P.","year":"2017","unstructured":"N. P. Jouppi et al., \"In-Datacenter Performance Analysis of a Tensor ProcessinUnit,\" in ISCA, 2017."},{"key":"e_1_3_2_1_2_1","volume-title":"3.3 Kunlun: A 14nm High-Performance AI Processor for Diversified Workloads,\" in ISSCC","author":"Ouyang J.","year":"2021","unstructured":"J. Ouyang et al., \"3.3 Kunlun: A 14nm High-Performance AI Processor for Diversified Workloads,\" in ISSCC, 2021."},{"key":"e_1_3_2_1_3_1","unstructured":"E. Technology. \"Enflame DTU \" https:\/\/www.servethehome.com\/enflame-dtu-1-0-ai-compute-chip-at-hot-chips-33\/."},{"key":"e_1_3_2_1_4_1","unstructured":"Cambricon. \"CambriconMLU290 \" https:\/\/www.cambricon.com\/index.php?m=content&c=index&a=lists&catid=340."},{"key":"e_1_3_2_1_5_1","volume-title":"BitX: Empower Versatile Inference with Hardware Runtime Pruning,\" in ICPP","author":"Li H.","year":"2021","unstructured":"H. Li et al., \"BitX: Empower Versatile Inference with Hardware Runtime Pruning,\" in ICPP, 2021."},{"key":"e_1_3_2_1_6_1","volume-title":"Bit-Pragmatic Deep Neural Network Computing,\" in MICRO","author":"Albericio J.","year":"2017","unstructured":"J. Albericio et al., \"Bit-Pragmatic Deep Neural Network Computing,\" in MICRO, 2017."},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/3240765.3240855"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"crossref","unstructured":"F. Tu et al. \"A 28nm 29.2TFLOPS\/W BF16 and 36.5TOPS\/W INT8 Reconfigurable Digital CIM Processor with Unified FP\/INT Pipeline and Bitwise In-Memory Booth Multiplication for Cloud Deep Learning Acceleration \" in ISSCC 2022.","DOI":"10.1109\/ISSCC42614.2022.9731762"},{"key":"e_1_3_2_1_9_1","volume-title":"Laconic deep learning inference acceleration,\" in ISCA","author":"Sharify S.","year":"2019","unstructured":"S. Sharify et al., \"Laconic deep learning inference acceleration,\" in ISCA, 2019."},{"key":"e_1_3_2_1_10_1","unstructured":"IEEE. \"IEEE Standard for Floating-Point Arithmetic (754-2019) \" https:\/\/standards.ieee.org\/standard\/754-2019.html."},{"key":"e_1_3_2_1_11_1","volume-title":"Distilling Bit-level Sparsity Parallelism for General Purpose Deep Learning Acceleration,\" in MICRO","author":"Lu H.","year":"2021","unstructured":"H. Lu et al., \"Distilling Bit-level Sparsity Parallelism for General Purpose Deep Learning Acceleration,\" in MICRO, 2021."},{"key":"e_1_3_2_1_12_1","volume-title":"ImageNet: A large-scale hierarchical image database,\" in CVPR","author":"Deng J.","year":"2009","unstructured":"J. Deng et al., \"ImageNet: A large-scale hierarchical image database,\" in CVPR, 2009."},{"key":"e_1_3_2_1_13_1","unstructured":"Facebook. \"Pytorch \" https:\/\/pytorch.org\/."},{"key":"e_1_3_2_1_14_1","volume-title":"Aggregated Residual Transformations for Deep Neural Networks,\" in CVPR","author":"Xie S.","year":"2017","unstructured":"S. Xie et al., \"Aggregated Residual Transformations for Deep Neural Networks,\" in CVPR, 2017."},{"key":"e_1_3_2_1_15_1","volume-title":"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale,\" in ICLR","author":"Dosovitskiy A.","year":"2020","unstructured":"A. Dosovitskiy et al., \"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale,\" in ICLR, 2020."},{"key":"e_1_3_2_1_16_1","volume-title":"Deformable 3D Convolution for Video Super-Resolution,\" arXiv:2004.02803","author":"Ying X.","year":"2020","unstructured":"X. Ying et al., \"Deformable 3D Convolution for Video Super-Resolution,\" arXiv:2004.02803, 2020."},{"key":"e_1_3_2_1_17_1","volume-title":"YOLOv3: An Incremental Improvement,\" in CVPR","author":"Redmon J.","year":"2018","unstructured":"J. Redmon, and A. Farhadi, \"YOLOv3: An Incremental Improvement,\" in CVPR, 2018."},{"key":"e_1_3_2_1_18_1","volume-title":"Microsoft COCO: Common Objects in Context,\" in ECCV","author":"Lin T.-Y.","year":"2014","unstructured":"T.-Y. Lin et al., \"Microsoft COCO: Common Objects in Context,\" in ECCV, 2014."},{"key":"e_1_3_2_1_19_1","volume-title":"Densely Connected Convolutional Networks,\" in CVPR","author":"Huang G.","year":"2017","unstructured":"G. Huang et al., \"Densely Connected Convolutional Networks,\" in CVPR, 2017."},{"key":"e_1_3_2_1_20_1","volume-title":"Deep Residual Learning for Image Recognition,\" in CVPR","author":"He K.","year":"2016","unstructured":"K. He et al., \"Deep Residual Learning for Image Recognition,\" in CVPR, 2016."},{"key":"e_1_3_2_1_21_1","volume-title":"SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and &lt;0.5MB model size,\" arXiv:1602.07360","author":"Iandola F.","year":"2010","unstructured":"F. Iandola et al., \"SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and &lt;0.5MB model size,\" arXiv:1602.07360, 2010."},{"key":"e_1_3_2_1_22_1","volume-title":"Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution,\" in CVPR","author":"Lai W.-S.","year":"2017","unstructured":"W.-S. Lai et al., \"Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution,\" in CVPR, 2017."},{"key":"e_1_3_2_1_23_1","volume-title":"CartoonGAN: Generative Adversarial Networks for Photo Cartoonization,\" in CVPR","author":"Chen Y.","year":"2018","unstructured":"Y. Chen et al., \"CartoonGAN: Generative Adversarial Networks for Photo Cartoonization,\" in CVPR, 2018."}],"event":{"name":"ASPDAC '23: 28th Asia and South Pacific Design Automation Conference","location":"Tokyo Japan","acronym":"ASPDAC '23","sponsor":["SIGDA ACM Special Interest Group on Design Automation","IEEE CEDA","IEICE","IEEE CAS","IPSJ"]},"container-title":["Proceedings of the 28th Asia and South Pacific Design Automation Conference"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3566097.3567868","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3566097.3567868","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,7]],"date-time":"2026-01-07T17:33:29Z","timestamp":1767807209000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3566097.3567868"}},"subtitle":["Morphing the Bit Level Sparsity for General Purpose Deep Learning Acceleration"],"short-title":[],"issued":{"date-parts":[[2023,1,16]]},"references-count":23,"alternative-id":["10.1145\/3566097.3567868","10.1145\/3566097"],"URL":"https:\/\/doi.org\/10.1145\/3566097.3567868","relation":{},"subject":[],"published":{"date-parts":[[2023,1,16]]},"assertion":[{"value":"2023-01-31","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}