{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,28]],"date-time":"2026-06-28T04:38:19Z","timestamp":1782621499211,"version":"3.54.5"},"publisher-location":"New York, NY, USA","reference-count":49,"publisher":"ACM","license":[{"start":{"date-parts":[[2019,11,17]],"date-time":"2019-11-17T00:00:00Z","timestamp":1573948800000},"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":[[2019,11,17]]},"DOI":"10.1145\/3295500.3356156","type":"proceedings-article","created":{"date-parts":[[2019,11,7]],"date-time":"2019-11-07T19:43:22Z","timestamp":1573155802000},"page":"1-13","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":50,"title":["PruneTrain"],"prefix":"10.1145","author":[{"given":"Sangkug","family":"Lym","sequence":"first","affiliation":[{"name":"The University of Texas at Austin"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Esha","family":"Choukse","sequence":"additional","affiliation":[{"name":"The University of Texas at Austin"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Siavash","family":"Zangeneh","sequence":"additional","affiliation":[{"name":"The University of Texas at Austin"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Wen","sequence":"additional","affiliation":[{"name":"Duke University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sujay","family":"Sanghavi","sequence":"additional","affiliation":[{"name":"The University of Texas at Austin"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mattan","family":"Erez","sequence":"additional","affiliation":[{"name":"The University of Texas at Austin"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2019,11,17]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Accurate, large minibatch sgd: Training imagenet in 1 hour,\" arXiv preprint arXiv:1706.02677","author":"Goyal P.","year":"2017","unstructured":"P. Goyal , P. Doll\u00e1r , R. Girshick , P. Noordhuis , L. Wesolowski , A. Kyrola , A. Tulloch , Y. Jia , and K. He , \" Accurate, large minibatch sgd: Training imagenet in 1 hour,\" arXiv preprint arXiv:1706.02677 , 2017 . P. Goyal, P. Doll\u00e1r, R. Girshick, P. Noordhuis, L. Wesolowski, A. Kyrola, A. Tulloch, Y. Jia, and K. He, \"Accurate, large minibatch sgd: Training imagenet in 1 hour,\" arXiv preprint arXiv:1706.02677, 2017."},{"key":"e_1_3_2_1_2_1","volume-title":"ACM","author":"You Y.","year":"2018","unstructured":"Y. You , Z. Zhang , C.-J. Hsieh , J. Demmel , and K. Keutzer , \" Imagenet training in minutes,\" in Proceedings of the 47th International Conference on Parallel Processing, p. 1 , ACM , 2018 . Y. You, Z. Zhang, C.-J. Hsieh, J. Demmel, and K. Keutzer, \"Imagenet training in minutes,\" in Proceedings of the 47th International Conference on Parallel Processing, p. 1, ACM, 2018."},{"key":"e_1_3_2_1_3_1","volume-title":"ImageNet: A Large-Scale Hierarchical Image Database,\" in CVPR09","author":"Deng J.","year":"2009","unstructured":"J. Deng , W. Dong , R. Socher , L.-J. Li , K. Li , and L. Fei-Fei , \" ImageNet: A Large-Scale Hierarchical Image Database,\" in CVPR09 , 2009 . J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, \"ImageNet: A Large-Scale Hierarchical Image Database,\" in CVPR09, 2009."},{"key":"e_1_3_2_1_4_1","volume-title":"Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding\" arXiv preprint arXiv:1510.00149","author":"Han S.","year":"2015","unstructured":"S. Han , H. Mao , and W. J. Dally , \" Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding\" arXiv preprint arXiv:1510.00149 , 2015 . S. Han, H. Mao, and W. J. Dally, \"Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding\" arXiv preprint arXiv:1510.00149, 2015."},{"key":"e_1_3_2_1_5_1","first-page":"1135","volume-title":"Learning both weights and connections for efficient neural network,\" in Advances in neural information processing systems","author":"Han S.","year":"2015","unstructured":"S. Han , J. Pool , J. Tran , and W. Dally , \" Learning both weights and connections for efficient neural network,\" in Advances in neural information processing systems , pp. 1135 -- 1143 , 2015 . S. Han, J. Pool, J. Tran, and W. Dally, \"Learning both weights and connections for efficient neural network,\" in Advances in neural information processing systems, pp. 1135--1143, 2015."},{"key":"e_1_3_2_1_6_1","first-page":"2074","volume-title":"Learning structured sparsity in deep neural networks,\" in Advances in Neural Information Processing Systems 29","author":"Wen W.","year":"2016","unstructured":"W. Wen , C. Wu , Y. Wang , Y. Chen , and H. Li , \" Learning structured sparsity in deep neural networks,\" in Advances in Neural Information Processing Systems 29 (D. D. Lee, M. Sugiyama, U. V. Luxburg, I. Guyon, and R. Garnett, eds.), pp. 2074 -- 2082 , Curran Associates, Inc. , 2016 . W. Wen, C. Wu, Y. Wang, Y. Chen, and H. Li, \"Learning structured sparsity in deep neural networks,\" in Advances in Neural Information Processing Systems 29 (D. D. Lee, M. Sugiyama, U. V. Luxburg, I. Guyon, and R. Garnett, eds.), pp. 2074--2082, Curran Associates, Inc., 2016."},{"key":"e_1_3_2_1_7_1","first-page":"2749","article-title":"\"Learning the structure of deep convolutional networks","author":"Feng J.","year":"2015","unstructured":"J. Feng and T. Darrell , \"Learning the structure of deep convolutional networks \" in Proceedings of the IEEE international conference on computer vision , pp. 2749 -- 2757 , 2015 . J. Feng and T. Darrell, \"Learning the structure of deep convolutional networks\" in Proceedings of the IEEE international conference on computer vision, pp. 2749--2757, 2015.","journal-title":"Proceedings of the IEEE international conference on computer vision"},{"key":"e_1_3_2_1_8_1","first-page":"856","volume-title":"Compression-aware training of deep networks,\" in Advances in Neural Information Processing Systems","author":"Alvarez J. M.","year":"2017","unstructured":"J. M. Alvarez and M. Salzmann , \" Compression-aware training of deep networks,\" in Advances in Neural Information Processing Systems , pp. 856 -- 867 , 2017 . J. M. Alvarez and M. Salzmann, \"Compression-aware training of deep networks,\" in Advances in Neural Information Processing Systems, pp. 856--867, 2017."},{"key":"e_1_3_2_1_9_1","volume-title":"Channel pruning for accelerating very deep neural networks,\" in International Conference on Computer Vision (ICCV)","author":"He Y.","year":"2017","unstructured":"Y. He , X. Zhang , and J. Sun , \" Channel pruning for accelerating very deep neural networks,\" in International Conference on Computer Vision (ICCV) , vol. 2 , 2017 . Y. He, X. Zhang, and J. Sun, \"Channel pruning for accelerating very deep neural networks,\" in International Conference on Computer Vision (ICCV), vol. 2, 2017."},{"key":"e_1_3_2_1_10_1","first-page":"784","volume-title":"Amc: Automl for model compression and acceleration on mobile devices,\" in Proceedings of the European Conference on Computer Vision (ECCV)","author":"He Y.","year":"2018","unstructured":"Y. He , J. Lin , Z. Liu , H. Wang , L.-J. Li , and S. Han , \" Amc: Automl for model compression and acceleration on mobile devices,\" in Proceedings of the European Conference on Computer Vision (ECCV) , pp. 784 -- 800 , 2018 . Y. He, J. Lin, Z. Liu, H. Wang, L.-J. Li, and S. Han, \"Amc: Automl for model compression and acceleration on mobile devices,\" in Proceedings of the European Conference on Computer Vision (ECCV), pp. 784--800, 2018."},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1467-9868.2005.00532.x"},{"key":"e_1_3_2_1_12_1","volume-title":"Learning intrinsic sparse structures within long short-term memory,\" arXiv preprint arXiv:1709.05027","author":"Wen W.","year":"2017","unstructured":"W. Wen , Y. He , S. Rajbhandari , M. Zhang , W. Wang , F. Liu , B. Hu , Y. Chen , and H. Li , \" Learning intrinsic sparse structures within long short-term memory,\" arXiv preprint arXiv:1709.05027 , 2017 . W. Wen, Y. He, S. Rajbhandari, M. Zhang, W. Wang, F. Liu, B. Hu, Y. Chen, and H. Li, \"Learning intrinsic sparse structures within long short-term memory,\" arXiv preprint arXiv:1709.05027, 2017."},{"key":"e_1_3_2_1_13_1","first-page":"662","volume-title":"Less is more: Towards compact cnns,\" in European Conference on Computer Vision","author":"Zhou H.","year":"2016","unstructured":"H. Zhou , J. M. Alvarez , and F. Porikli , \" Less is more: Towards compact cnns,\" in European Conference on Computer Vision , pp. 662 -- 677 , Springer , 2016 . H. Zhou, J. M. Alvarez, and F. Porikli, \"Less is more: Towards compact cnns,\" in European Conference on Computer Vision, pp. 662--677, Springer, 2016."},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1467-9868.2007.00627.x"},{"key":"e_1_3_2_1_15_1","first-page":"770","article-title":"Deep residual learning for image recognition","author":"He K.","year":"2016","unstructured":"K. He , X. Zhang , S. Ren , and J. Sun , \" Deep residual learning for image recognition ,\" in Proceedings of the IEEE conference on computer vision and pattern recognition , pp. 770 -- 778 , 2016 . K. He, X. Zhang, S. Ren, and J. Sun, \"Deep residual learning for image recognition,\" in Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 770--778, 2016.","journal-title":"Proceedings of the IEEE conference on computer vision and pattern recognition"},{"key":"e_1_3_2_1_16_1","first-page":"3","article-title":"Van Der Maaten, and K. Q. Weinberger, \"Densely connected convolutional networks","volume":"1","author":"Huang G.","year":"2017","unstructured":"G. Huang , Z. Liu , L . Van Der Maaten, and K. Q. Weinberger, \"Densely connected convolutional networks .,\" in CVPR , vol. 1 , p. 3 , 2017 . G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger, \"Densely connected convolutional networks.,\" in CVPR, vol. 1, p. 3, 2017.","journal-title":"CVPR"},{"key":"e_1_3_2_1_17_1","first-page":"7132","article-title":"Squeeze-and-excitation networks","author":"Hu J.","year":"2018","unstructured":"J. Hu , L. Shen , and G. Sun , \" Squeeze-and-excitation networks ,\" in Proceedings of the IEEE conference on computer vision and pattern recognition , pp. 7132 -- 7141 , 2018 . J. Hu, L. Shen, and G. Sun, \"Squeeze-and-excitation networks,\" in Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 7132--7141, 2018.","journal-title":"Proceedings of the IEEE conference on computer vision and pattern recognition"},{"key":"e_1_3_2_1_18_1","volume-title":"Wide residual networks,\" arXiv preprint arXiv:1605.07146","author":"Zagoruyko S.","year":"2016","unstructured":"S. Zagoruyko and N. Komodakis , \" Wide residual networks,\" arXiv preprint arXiv:1605.07146 , 2016 . S. Zagoruyko and N. Komodakis, \"Wide residual networks,\" arXiv preprint arXiv:1605.07146, 2016."},{"key":"e_1_3_2_1_19_1","volume-title":"Don't decay the learning rate, increase the batch size,\" arXiv preprint arXiv:1711.00489","author":"Smith S. L.","year":"2017","unstructured":"S. L. Smith , P.-J. Kindermans , C. Ying , and Q. V. Le , \" Don't decay the learning rate, increase the batch size,\" arXiv preprint arXiv:1711.00489 , 2017 . S. L. Smith, P.-J. Kindermans, C. Ying, and Q. V. Le, \"Don't decay the learning rate, increase the batch size,\" arXiv preprint arXiv:1711.00489, 2017."},{"key":"e_1_3_2_1_20_1","volume-title":"Batch normalization: Accelerating deep network training by reducing internal covariate shift,\" arXiv preprint arXiv:1502.03167","author":"Ioffe S.","year":"2015","unstructured":"S. Ioffe and C. Szegedy , \" Batch normalization: Accelerating deep network training by reducing internal covariate shift,\" arXiv preprint arXiv:1502.03167 , 2015 . S. Ioffe and C. Szegedy, \"Batch normalization: Accelerating deep network training by reducing internal covariate shift,\" arXiv preprint arXiv:1502.03167, 2015."},{"key":"e_1_3_2_1_21_1","volume-title":"Norm matters: efficient and accurate normalization schemes in deep networks,\" arXiv preprint arXiv:1803.01814","author":"Hoffer E.","year":"2018","unstructured":"E. Hoffer , R. Banner , I. Golan , and D. Soudry , \" Norm matters: efficient and accurate normalization schemes in deep networks,\" arXiv preprint arXiv:1803.01814 , 2018 . E. Hoffer, R. Banner, I. Golan, and D. Soudry, \"Norm matters: efficient and accurate normalization schemes in deep networks,\" arXiv preprint arXiv:1803.01814, 2018."},{"key":"e_1_3_2_1_22_1","unstructured":"S. Ruder \"An overview of gradient descent optimization algorithms \" arXiv preprint arXiv:1609.04747 2016.  S. Ruder \"An overview of gradient descent optimization algorithms \" arXiv preprint arXiv:1609.04747 2016."},{"key":"e_1_3_2_1_23_1","volume-title":"ACM","author":"Li M.","year":"2014","unstructured":"M. Li , T. Zhang , Y. Chen , and A. J. Smola , \" Efficient mini-batch training for stochastic optimization,\" in Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 661--670 , ACM , 2014 . M. Li, T. Zhang, Y. Chen, and A. J. Smola, \"Efficient mini-batch training for stochastic optimization,\" in Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 661--670, ACM, 2014."},{"key":"e_1_3_2_1_24_1","first-page":"1097","volume-title":"Imagenet classification with deep convolutional neural networks,\" in Advances in neural information processing systems","author":"Krizhevsky A.","year":"2012","unstructured":"A. Krizhevsky , I. Sutskever , and G. E. Hinton , \" Imagenet classification with deep convolutional neural networks,\" in Advances in neural information processing systems , pp. 1097 -- 1105 , 2012 . A. Krizhevsky, I. Sutskever, and G. E. Hinton, \"Imagenet classification with deep convolutional neural networks,\" in Advances in neural information processing systems, pp. 1097--1105, 2012."},{"key":"e_1_3_2_1_25_1","first-page":"1509","volume-title":"Terngrad: Ternary gradients to reduce communication in distributed deep learning,\" in Advances in neural information processing systems","author":"Wen W.","year":"2017","unstructured":"W. Wen , C. Xu , F. Yan , C. Wu , Y. Wang , Y. Chen , and H. Li , \" Terngrad: Ternary gradients to reduce communication in distributed deep learning,\" in Advances in neural information processing systems , pp. 1509 -- 1519 , 2017 . W. Wen, C. Xu, F. Yan, C. Wu, Y. Wang, Y. Chen, and H. Li, \"Terngrad: Ternary gradients to reduce communication in distributed deep learning,\" in Advances in neural information processing systems, pp. 1509--1519, 2017."},{"key":"e_1_3_2_1_26_1","first-page":"175","volume-title":"A network-centric hardware\/algorithm co-design to accelerate distributed training of deep neural networks,\" in 2018 51st Annual IEEE\/ACM International Symposium on Microarchitecture (MICRO)","author":"Li Y.","year":"2018","unstructured":"Y. Li , J. Park , M. Alian , Y. Yuan , Z. Qu , P. Pan , R. Wang , A. Schwing , H. Esmaeilzadeh , and N. S. Kim , \" A network-centric hardware\/algorithm co-design to accelerate distributed training of deep neural networks,\" in 2018 51st Annual IEEE\/ACM International Symposium on Microarchitecture (MICRO) , pp. 175 -- 188 , IEEE , 2018 . Y. Li, J. Park, M. Alian, Y. Yuan, Z. Qu, P. Pan, R. Wang, A. Schwing, H. Esmaeilzadeh, and N. S. Kim, \"A network-centric hardware\/algorithm co-design to accelerate distributed training of deep neural networks,\" in 2018 51st Annual IEEE\/ACM International Symposium on Microarchitecture (MICRO), pp. 175--188, IEEE, 2018."},{"key":"e_1_3_2_1_27_1","volume-title":"Mini-batch serialization: Cnn training with inter-layer data reuse,\" arXiv preprint arXiv:1810.00307","author":"Lym S.","year":"2018","unstructured":"S. Lym , A. Behroozi , W. Wen , G. Li , Y. Kwon , and M. Erez , \" Mini-batch serialization: Cnn training with inter-layer data reuse,\" arXiv preprint arXiv:1810.00307 , 2018 . S. Lym, A. Behroozi, W. Wen, G. Li, Y. Kwon, and M. Erez, \"Mini-batch serialization: Cnn training with inter-layer data reuse,\" arXiv preprint arXiv:1810.00307, 2018."},{"key":"e_1_3_2_1_28_1","volume-title":"ACM","author":"Han S.","year":"2017","unstructured":"S. Han , J. Kang , H. Mao , Y. Hu , X. Li , Y. Li , D. Xie , H. Luo , S. Yao , Y. Wang , : Efficient speech recognition engine with sparse lstm on fpga,\" in Proceedings of the 2017 ACM\/SIGDA International Symposium on Field-Programmable Gate Arrays, pp. 75--84 , ACM , 2017 . S. Han, J. Kang, H. Mao, Y. Hu, X. Li, Y. Li, D. Xie, H. Luo, S. Yao, Y. Wang, et al., \"Ese: Efficient speech recognition engine with sparse lstm on fpga,\" in Proceedings of the 2017 ACM\/SIGDA International Symposium on Field-Programmable Gate Arrays, pp. 75--84, ACM, 2017."},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1145\/3140659.3080215"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1145\/3005348"},{"key":"e_1_3_2_1_31_1","volume-title":"A data-driven neuron pruning approach towards efficient deep architectures,\" arXiv preprint arXiv:1607.03250","author":"Tai H.","year":"2016","unstructured":"H. Hu R. Peng Y.-W. Tai and C.-K. Tang \"Network trimming : A data-driven neuron pruning approach towards efficient deep architectures,\" arXiv preprint arXiv:1607.03250 , 2016 . H. Hu R. Peng Y.-W. Tai and C.-K. Tang \"Network trimming: A data-driven neuron pruning approach towards efficient deep architectures,\" arXiv preprint arXiv:1607.03250, 2016."},{"key":"e_1_3_2_1_32_1","volume-title":"Pruning convolutional neural networks for resource efficient transfer learning,\" CoRR, abs\/1611.06440","author":"Molchanov P.","year":"2016","unstructured":"P. Molchanov , S. Tyree , T. Karras , T. Aila , and J. Kautz , \" Pruning convolutional neural networks for resource efficient transfer learning,\" CoRR, abs\/1611.06440 , 2016 . P. Molchanov, S. Tyree, T. Karras, T. Aila, and J. Kautz, \"Pruning convolutional neural networks for resource efficient transfer learning,\" CoRR, abs\/1611.06440, 2016."},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.2517-6161.1996.tb02080.x"},{"key":"e_1_3_2_1_34_1","first-page":"2980","article-title":"Focal loss for dense object detection","author":"Lin T.-Y.","year":"2017","unstructured":"T.-Y. Lin , P. Goyal , R. Girshick , K. He , and P. Doll\u00e1r , \" Focal loss for dense object detection ,\" in Proceedings of the IEEE international conference on computer vision , pp. 2980 -- 2988 , 2017 . T.-Y. Lin, P. Goyal, R. Girshick, K. He, and P. Doll\u00e1r, \"Focal loss for dense object detection,\" in Proceedings of the IEEE international conference on computer vision, pp. 2980--2988, 2017.","journal-title":"Proceedings of the IEEE international conference on computer vision"},{"key":"e_1_3_2_1_35_1","first-page":"2961","article-title":"Mask r-cnn","author":"He K.","year":"2017","unstructured":"K. He , G. Gkioxari , P. Doll\u00e1r , and R. Girshick , \" Mask r-cnn ,\" in Proceedings of the IEEE international conference on computer vision , pp. 2961 -- 2969 , 2017 . K. He, G. Gkioxari, P. Doll\u00e1r, and R. Girshick, \"Mask r-cnn,\" in Proceedings of the IEEE international conference on computer vision, pp. 2961--2969, 2017.","journal-title":"Proceedings of the IEEE international conference on computer vision"},{"key":"e_1_3_2_1_36_1","first-page":"2117","article-title":"Feature pyramid networks for object detection","author":"Lin T.-Y.","year":"2017","unstructured":"T.-Y. Lin , P. Doll\u00e1r , R. Girshick , K. He , B. Hariharan , and S. Belongie , \" Feature pyramid networks for object detection ,\" in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , pp. 2117 -- 2125 , 2017 . T.-Y. Lin, P. Doll\u00e1r, R. Girshick, K. He, B. Hariharan, and S. Belongie, \"Feature pyramid networks for object detection,\" in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117--2125, 2017.","journal-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition"},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.5705\/ss.2011.075"},{"key":"e_1_3_2_1_38_1","first-page":"2270","volume-title":"Learning the number of neurons in deep networks,\" in Advances in Neural Information Processing Systems","author":"Alvarez J. M.","year":"2016","unstructured":"J. M. Alvarez and M. Salzmann , \" Learning the number of neurons in deep networks,\" in Advances in Neural Information Processing Systems , pp. 2270 -- 2278 , 2016 . J. M. Alvarez and M. Salzmann, \"Learning the number of neurons in deep networks,\" in Advances in Neural Information Processing Systems, pp. 2270--2278, 2016."},{"key":"e_1_3_2_1_39_1","first-page":"630","volume-title":"Identity mappings in deep residual networks,\" in European conference on computer vision","author":"He K.","year":"2016","unstructured":"K. He , X. Zhang , S. Ren , and J. Sun , \" Identity mappings in deep residual networks,\" in European conference on computer vision , pp. 630 -- 645 , Springer , 2016 . K. He, X. Zhang, S. Ren, and J. Sun, \"Identity mappings in deep residual networks,\" in European conference on computer vision, pp. 630--645, Springer, 2016."},{"key":"e_1_3_2_1_40_1","first-page":"5987","volume-title":"2017 IEEE Conference on","author":"Xie S.","year":"2017","unstructured":"S. Xie , R. Girshick , P. Doll\u00e1r , Z. Tu , and K. He , \" Aggregated residual transformations for deep neural networks,\" in Computer Vision and Pattern Recognition (CVPR) , 2017 IEEE Conference on , pp. 5987 -- 5995 , IEEE, 2017 . S. Xie, R. Girshick, P. Doll\u00e1r, Z. Tu, and K. He, \"Aggregated residual transformations for deep neural networks,\" in Computer Vision and Pattern Recognition (CVPR), 2017 IEEE Conference on, pp. 5987--5995, IEEE, 2017."},{"key":"e_1_3_2_1_41_1","volume-title":"Squeeze-and-excitation networks,\" arXiv preprint arXiv:1709.01507","author":"Hu J.","year":"2017","unstructured":"J. Hu , L. Shen , and G. Sun , \" Squeeze-and-excitation networks,\" arXiv preprint arXiv:1709.01507 , vol. 7 , 2017 . J. Hu, L. Shen, and G. Sun, \"Squeeze-and-excitation networks,\" arXiv preprint arXiv:1709.01507, vol. 7, 2017."},{"key":"e_1_3_2_1_42_1","first-page":"290","volume-title":"Large scale language modeling: Converging on 40gb of text in four hours,\" in 2018 30th International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD)","author":"Puri R.","year":"2018","unstructured":"R. Puri , R. Kirby , N. Yakovenko , and B. Catanzaro , \" Large scale language modeling: Converging on 40gb of text in four hours,\" in 2018 30th International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD) , pp. 290 -- 297 , IEEE , 2018 . R. Puri, R. Kirby, N. Yakovenko, and B. Catanzaro, \"Large scale language modeling: Converging on 40gb of text in four hours,\" in 2018 30th International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD), pp. 290--297, IEEE, 2018."},{"key":"e_1_3_2_1_43_1","volume-title":"Citeseer","author":"Krizhevsky A.","year":"2009","unstructured":"A. Krizhevsky and G. Hinton , \" Learning multiple layers of features from tiny images,\" tech. rep ., Citeseer , 2009 . A. Krizhevsky and G. Hinton, \"Learning multiple layers of features from tiny images,\" tech. rep., Citeseer, 2009."},{"key":"e_1_3_2_1_44_1","unstructured":"nvidia \"Nvidia tesla v100 gpu architecture \" White paper 2017.  nvidia \"Nvidia tesla v100 gpu architecture \" White paper 2017."},{"key":"e_1_3_2_1_45_1","unstructured":"nvidia \"Nvidia tesla p100 gpu architecture \" White paper 2016.  nvidia \"Nvidia tesla p100 gpu architecture \" White paper 2016."},{"key":"e_1_3_2_1_46_1","volume-title":"Automatic differentiation in pytorch,\" in NIPS-W","author":"Paszke A.","year":"2017","unstructured":"A. Paszke , S. Gross , S. Chintala , G. Chanan , E. Yang , Z. DeVito , Z. Lin , A. Desmaison , L. Antiga , and A. Lerer , \" Automatic differentiation in pytorch,\" in NIPS-W , 2017 . A. Paszke, S. Gross, S. Chintala, G. Chanan, E. Yang, Z. DeVito, Z. Lin, A. Desmaison, L. Antiga, and A. Lerer, \"Automatic differentiation in pytorch,\" in NIPS-W, 2017."},{"key":"e_1_3_2_1_47_1","volume-title":"JESD235A","author":"Joint Electron Device Engineering Council","year":"2016","unstructured":"Joint Electron Device Engineering Council , High Bandwidth Memory (HBM) DRAM , JESD235A , Jan. 2016 . Joint Electron Device Engineering Council, High Bandwidth Memory (HBM) DRAM, JESD235A, Jan. 2016."},{"key":"e_1_3_2_1_48_1","volume-title":"Mixed precision training,\" arXiv preprint arXiv:1710.03740","author":"Micikevicius P.","year":"2017","unstructured":"P. Micikevicius , S. Narang , J. Alben , G. Diamos , E. Elsen , D. Garcia , B. Ginsburg , M. Houston , O. Kuchaiev , G. Venkatesh , , \" Mixed precision training,\" arXiv preprint arXiv:1710.03740 , 2017 . P. Micikevicius, S. Narang, J. Alben, G. Diamos, E. Elsen, D. Garcia, B. Ginsburg, M. Houston, O. Kuchaiev, G. Venkatesh, et al., \"Mixed precision training,\" arXiv preprint arXiv:1710.03740, 2017."},{"key":"e_1_3_2_1_49_1","first-page":"243","volume-title":"2016 ACM\/IEEE 43rd Annual International Symposium on","author":"Han S.","year":"2016","unstructured":"S. Han , X. Liu , H. Mao , J. Pu , A. Pedram , M. A. Horowitz , and W. J. Dally , \" Eie: efficient inference engine on compressed deep neural network,\" in Computer Architecture (ISCA) , 2016 ACM\/IEEE 43rd Annual International Symposium on , pp. 243 -- 254 , IEEE, 2016 . S. Han, X. Liu, H. Mao, J. Pu, A. Pedram, M. A. Horowitz, and W. J. Dally, \"Eie: efficient inference engine on compressed deep neural network,\" in Computer Architecture (ISCA), 2016 ACM\/IEEE 43rd Annual International Symposium on, pp. 243--254, IEEE, 2016."}],"event":{"name":"SC '19: The International Conference for High Performance Computing, Networking, Storage, and Analysis","location":"Denver Colorado","acronym":"SC '19","sponsor":["SIGHPC ACM Special Interest Group on High Performance Computing, Special Interest Group on High Performance Computing","IEEE CS"]},"container-title":["Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3295500.3356156","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3295500.3356156","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T01:02:13Z","timestamp":1750208533000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3295500.3356156"}},"subtitle":["fast neural network training by dynamic sparse model reconfiguration"],"short-title":[],"issued":{"date-parts":[[2019,11,17]]},"references-count":49,"alternative-id":["10.1145\/3295500.3356156","10.1145\/3295500"],"URL":"https:\/\/doi.org\/10.1145\/3295500.3356156","relation":{},"subject":[],"published":{"date-parts":[[2019,11,17]]},"assertion":[{"value":"2019-11-17","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}