{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T04:46:50Z","timestamp":1750308410650,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":37,"publisher":"ACM","license":[{"start":{"date-parts":[[2021,8,9]],"date-time":"2021-08-09T00:00:00Z","timestamp":1628467200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2021,8,9]]},"DOI":"10.1145\/3458744.3473352","type":"proceedings-article","created":{"date-parts":[[2021,9,23]],"date-time":"2021-09-23T16:38:30Z","timestamp":1632415110000},"page":"1-7","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":8,"title":["Support Convolution of CNN with Compression Sparse Matrix Multiplication Flow in TVM"],"prefix":"10.1145","author":[{"given":"Hui-Hsin","family":"Liao","sequence":"first","affiliation":[{"name":"National Tsing Hua University, Taiwan"}]},{"given":"Chao-Lin","family":"Lee","sequence":"additional","affiliation":[{"name":"National Tsing Hua University, Taiwan"}]},{"given":"Jenq-Kuen","family":"Lee","sequence":"additional","affiliation":[{"name":"National Tsing Hua University, Taiwan"}]},{"given":"Wei-Chih","family":"Lai","sequence":"additional","affiliation":[{"name":"MediaTek Inc, Taiwan"}]},{"given":"Ming-Yu","family":"Hung","sequence":"additional","affiliation":[{"name":"MediaTek Inc, Taiwan"}]},{"given":"Chung-Wen","family":"Huang","sequence":"additional","affiliation":[{"name":"MediaTek Inc, Taiwan"}]}],"member":"320","published-online":{"date-parts":[[2021,9,23]]},"reference":[{"unstructured":"Mart\u00edn Abadi and Ashish\u00a0Agarwal et al.2016. TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems. arxiv:1603.04467\u00a0[cs.DC]  Mart\u00edn Abadi and Ashish\u00a0Agarwal et al.2016. TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems. arxiv:1603.04467\u00a0[cs.DC]","key":"e_1_3_2_1_1_1"},{"key":"e_1_3_2_1_2_1","volume-title":"Automation & Test in Europe Conference & Exhibition (DATE). IEEE, 73\u201378","author":"Ahn Byungmin","year":"2020","unstructured":"Byungmin Ahn and Taewhan Kim . 2020 . Deeper weight pruning without accuracy loss in deep neural networks. In 2020 Design , Automation & Test in Europe Conference & Exhibition (DATE). IEEE, 73\u201378 . Byungmin Ahn and Taewhan Kim. 2020. Deeper weight pruning without accuracy loss in deep neural networks. In 2020 Design, Automation & Test in Europe Conference & Exhibition (DATE). IEEE, 73\u201378."},{"unstructured":"Andrew Anderson Aravind Vasudevan Cormac Keane and David Gregg. 2017. Low-memory GEMM-based convolution algorithms for deep neural networks. arxiv:1709.03395\u00a0[cs.CV]  Andrew Anderson Aravind Vasudevan Cormac Keane and David Gregg. 2017. Low-memory GEMM-based convolution algorithms for deep neural networks. arxiv:1709.03395\u00a0[cs.CV]","key":"e_1_3_2_1_3_1"},{"key":"e_1_3_2_1_4_1","volume-title":"ONNX: Open Neural Network Exchange. https:\/\/github.com\/onnx\/onnx.","author":"Bai Junjie","year":"2019","unstructured":"Junjie Bai , Fang Lu , Ke Zhang , 2019 . ONNX: Open Neural Network Exchange. https:\/\/github.com\/onnx\/onnx. Junjie Bai, Fang Lu, Ke Zhang, 2019. ONNX: Open Neural Network Exchange. https:\/\/github.com\/onnx\/onnx."},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_5_1","DOI":"10.1016\/j.parco.2014.03.012"},{"unstructured":"Tianqi Chen Mu Li Yutian Li Min Lin Naiyan Wang Minjie Wang Tianjun Xiao Bing Xu Chiyuan Zhang and Zheng Zhang. 2015. MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems. arxiv:1512.01274\u00a0[cs.DC]  Tianqi Chen Mu Li Yutian Li Min Lin Naiyan Wang Minjie Wang Tianjun Xiao Bing Xu Chiyuan Zhang and Zheng Zhang. 2015. MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems. arxiv:1512.01274\u00a0[cs.DC]","key":"e_1_3_2_1_6_1"},{"key":"e_1_3_2_1_7_1","volume-title":"TVM: An Automated End-to-End Optimizing Compiler for Deep Learning. arxiv:1802.04799\u00a0[cs.LG]","author":"Chen Tianqi","year":"2018","unstructured":"Tianqi Chen , Thierry Moreau , Ziheng Jiang , Lianmin Zheng , Eddie Yan , Meghan Cowan , Haichen Shen , Leyuan Wang , Yuwei Hu , Luis Ceze , Carlos Guestrin , and Arvind Krishnamurthy . 2018 . TVM: An Automated End-to-End Optimizing Compiler for Deep Learning. arxiv:1802.04799\u00a0[cs.LG] Tianqi Chen, Thierry Moreau, Ziheng Jiang, Lianmin Zheng, Eddie Yan, Meghan Cowan, Haichen Shen, Leyuan Wang, Yuwei Hu, Luis Ceze, Carlos Guestrin, and Arvind Krishnamurthy. 2018. TVM: An Automated End-to-End Optimizing Compiler for Deep Learning. arxiv:1802.04799\u00a0[cs.LG]"},{"unstructured":"Francois Chollet 2015. Keras. https:\/\/github.com\/fchollet\/keras  Francois Chollet 2015. Keras. https:\/\/github.com\/fchollet\/keras","key":"e_1_3_2_1_8_1"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_9_1","DOI":"10.1109\/CVPR.2009.5206848"},{"doi-asserted-by":"crossref","unstructured":"Aiyoub Farzaneh H. Kheiri and Mehdi\u00a0Abbaspour Shahmersi. 2009. AN EFFICIENT STORAGE FORMAT FOR LARGE SPARSE MATRICES.  Aiyoub Farzaneh H. Kheiri and Mehdi\u00a0Abbaspour Shahmersi. 2009. AN EFFICIENT STORAGE FORMAT FOR LARGE SPARSE MATRICES.","key":"e_1_3_2_1_10_1","DOI":"10.1501\/Commua1_0000000648"},{"unstructured":"Yiwen Guo Anbang Yao and Yurong Chen. 2016. Dynamic Network Surgery for Efficient DNNs. arxiv:1608.04493\u00a0[cs.NE]  Yiwen Guo Anbang Yao and Yurong Chen. 2016. Dynamic Network Surgery for Efficient DNNs. arxiv:1608.04493\u00a0[cs.NE]","key":"e_1_3_2_1_11_1"},{"unstructured":"Song Han Jeff Pool John Tran and William\u00a0J. Dally. 2015. Learning both Weights and Connections for Efficient Neural Networks. arxiv:1506.02626\u00a0[cs.NE]  Song Han Jeff Pool John Tran and William\u00a0J. Dally. 2015. Learning both Weights and Connections for Efficient Neural Networks. arxiv:1506.02626\u00a0[cs.NE]","key":"e_1_3_2_1_12_1"},{"unstructured":"Kaiming He Xiangyu Zhang Shaoqing Ren and Jian Sun. 2015. Deep Residual Learning for Image Recognition. arxiv:1512.03385\u00a0[cs.CV]  Kaiming He Xiangyu Zhang Shaoqing Ren and Jian Sun. 2015. Deep Residual Learning for Image Recognition. arxiv:1512.03385\u00a0[cs.CV]","key":"e_1_3_2_1_13_1"},{"doi-asserted-by":"crossref","unstructured":"Tyler Highlander and Andres Rodriguez. 2016. Very Efficient Training of Convolutional Neural Networks using Fast Fourier Transform and Overlap-and-Add. arxiv:1601.06815\u00a0[cs.NE]  Tyler Highlander and Andres Rodriguez. 2016. Very Efficient Training of Convolutional Neural Networks using Fast Fourier Transform and Overlap-and-Add. arxiv:1601.06815\u00a0[cs.NE]","key":"e_1_3_2_1_14_1","DOI":"10.5244\/C.29.160"},{"unstructured":"Andrew\u00a0G. Howard Menglong Zhu Bo Chen Dmitry Kalenichenko Weijun Wang Tobias Weyand Marco Andreetto and Hartwig Adam. 2017. MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arxiv:1704.04861\u00a0[cs.CV]  Andrew\u00a0G. Howard Menglong Zhu Bo Chen Dmitry Kalenichenko Weijun Wang Tobias Weyand Marco Andreetto and Hartwig Adam. 2017. MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arxiv:1704.04861\u00a0[cs.CV]","key":"e_1_3_2_1_15_1"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_16_1","DOI":"10.1109\/ESTIMedia.2014.6962353"},{"unstructured":"Alex Krizhevsky Geoffrey Hinton 2009. Learning multiple layers of features from tiny images. (2009).  Alex Krizhevsky Geoffrey Hinton 2009. Learning multiple layers of features from tiny images. (2009).","key":"e_1_3_2_1_17_1"},{"key":"e_1_3_2_1_18_1","volume-title":"Inducing and Exploiting Activation Sparsity for Fast Inference on Deep Neural Networks. In International Conference on Machine Learning. PMLR, 5533\u20135543","author":"Kurtz Mark","year":"2020","unstructured":"Mark Kurtz , Justin Kopinsky , Rati Gelashvili , Alexander Matveev , John Carr , Michael Goin , William Leiserson , Sage Moore , Nir Shavit , and Dan Alistarh . 2020 . Inducing and Exploiting Activation Sparsity for Fast Inference on Deep Neural Networks. In International Conference on Machine Learning. PMLR, 5533\u20135543 . Mark Kurtz, Justin Kopinsky, Rati Gelashvili, Alexander Matveev, John Carr, Michael Goin, William Leiserson, Sage Moore, Nir Shavit, and Dan Alistarh. 2020. Inducing and Exploiting Activation Sparsity for Fast Inference on Deep Neural Networks. In International Conference on Machine Learning. PMLR, 5533\u20135543."},{"unstructured":"Andrew Lavin and Scott Gray. 2015. Fast Algorithms for Convolutional Neural Networks. arxiv:1509.09308\u00a0[cs.NE]  Andrew Lavin and Scott Gray. 2015. Fast Algorithms for Convolutional Neural Networks. arxiv:1509.09308\u00a0[cs.NE]","key":"e_1_3_2_1_19_1"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_20_1","DOI":"10.1145\/3318170.3318179"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_21_1","DOI":"10.1145\/3339186.3339194"},{"unstructured":"Hao Li Asim Kadav Igor Durdanovic Hanan Samet and Hans\u00a0Peter Graf. 2017. Pruning Filters for Efficient ConvNets. arxiv:1608.08710\u00a0[cs.CV]  Hao Li Asim Kadav Igor Durdanovic Hanan Samet and Hans\u00a0Peter Graf. 2017. Pruning Filters for Efficient ConvNets. arxiv:1608.08710\u00a0[cs.CV]","key":"e_1_3_2_1_22_1"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_23_1","DOI":"10.1145\/3299874.3319492"},{"unstructured":"Wen Li Limin Wang Wei Li Eirikur Agustsson and Luc\u00a0Van Gool. 2017. WebVision Database: Visual Learning and Understanding from Web Data. arxiv:1708.02862\u00a0[cs.CV]  Wen Li Limin Wang Wei Li Eirikur Agustsson and Luc\u00a0Van Gool. 2017. WebVision Database: Visual Learning and Understanding from Web Data. arxiv:1708.02862\u00a0[cs.CV]","key":"e_1_3_2_1_24_1"},{"unstructured":"Xingyu Liu Jeff Pool Song Han and William\u00a0J. Dally. 2018. Efficient Sparse-Winograd Convolutional Neural Networks. arxiv:1802.06367\u00a0[cs.CV]  Xingyu Liu Jeff Pool Song Han and William\u00a0J. Dally. 2018. Efficient Sparse-Winograd Convolutional Neural Networks. arxiv:1802.06367\u00a0[cs.CV]","key":"e_1_3_2_1_25_1"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_26_1","DOI":"10.1145\/3373376.3378534"},{"unstructured":"Adam Paszke Sam Gross Soumith Chintala Gregory Chanan Edward Yang Zachary DeVito Zeming Lin Alban Desmaison Luca Antiga and Adam Lerer. 2017. Automatic differentiation in pytorch. (2017).  Adam Paszke Sam Gross Soumith Chintala Gregory Chanan Edward Yang Zachary DeVito Zeming Lin Alban Desmaison Luca Antiga and Adam Lerer. 2017. Automatic differentiation in pytorch. (2017).","key":"e_1_3_2_1_27_1"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_28_1","DOI":"10.1145\/3150211"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_29_1","DOI":"10.1145\/3211346.3211348"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_30_1","DOI":"10.1145\/3410463.3414648"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_31_1","DOI":"10.1007\/s11263-015-0816-y"},{"unstructured":"Karen Simonyan and Andrew Zisserman. 2015. Very Deep Convolutional Networks for Large-Scale Image Recognition. arxiv:1409.1556\u00a0[cs.CV]  Karen Simonyan and Andrew Zisserman. 2015. Very Deep Convolutional Networks for Large-Scale Image Recognition. arxiv:1409.1556\u00a0[cs.CV]","key":"e_1_3_2_1_32_1"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_33_1","DOI":"10.1109\/ASAP.2017.7995254"},{"unstructured":"Wei Wen Chunpeng Wu Yandan Wang Yiran Chen and Hai Li. 2016. Learning Structured Sparsity in Deep Neural Networks. arxiv:1608.03665\u00a0[cs.NE]  Wei Wen Chunpeng Wu Yandan Wang Yiran Chen and Hai Li. 2016. Learning Structured Sparsity in Deep Neural Networks. arxiv:1608.03665\u00a0[cs.NE]","key":"e_1_3_2_1_34_1"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_35_1","DOI":"10.1109\/ICASSP.2012.6288897"},{"unstructured":"Michael Zhu and Suyog Gupta. 2017. To prune or not to prune: exploring the efficacy of pruning for model compression. arxiv:1710.01878\u00a0[stat.ML]  Michael Zhu and Suyog Gupta. 2017. To prune or not to prune: exploring the efficacy of pruning for model compression. arxiv:1710.01878\u00a0[stat.ML]","key":"e_1_3_2_1_36_1"},{"unstructured":"Neta Zmora Guy Jacob Lev Zlotnik Bar Elharar and Gal Novik. 2019. Neural Network Distiller: A Python Package For DNN Compression Research. ArXiv abs\/1910.12232(2019).  Neta Zmora Guy Jacob Lev Zlotnik Bar Elharar and Gal Novik. 2019. Neural Network Distiller: A Python Package For DNN Compression Research. ArXiv abs\/1910.12232(2019).","key":"e_1_3_2_1_37_1"}],"event":{"acronym":"ICPP 2021","name":"ICPP 2021: 50th International Conference on Parallel Processing","location":"Lemont IL USA"},"container-title":["50th International Conference on Parallel Processing Workshop"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3458744.3473352","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3458744.3473352","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T17:49:06Z","timestamp":1750268946000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3458744.3473352"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,8,9]]},"references-count":37,"alternative-id":["10.1145\/3458744.3473352","10.1145\/3458744"],"URL":"https:\/\/doi.org\/10.1145\/3458744.3473352","relation":{},"subject":[],"published":{"date-parts":[[2021,8,9]]},"assertion":[{"value":"2021-09-23","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}