{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:14:50Z","timestamp":1750220090080,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":37,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,6,27]],"date-time":"2022-06-27T00:00:00Z","timestamp":1656288000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000015","name":"DOE U.S. Department of Energy","doi-asserted-by":"publisher","award":["DEAC02-06CH11357"],"award-info":[{"award-number":["DEAC02-06CH11357"]}],"id":[{"id":"10.13039\/100000015","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,6,27]]},"DOI":"10.1145\/3502181.3531463","type":"proceedings-article","created":{"date-parts":[[2022,6,23]],"date-time":"2022-06-23T22:14:30Z","timestamp":1656022470000},"page":"265-276","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["Efficient Design Space Exploration for Sparse Mixed Precision Neural Architectures"],"prefix":"10.1145","author":[{"given":"Krishna Teja","family":"Chitty-Venkata","sequence":"first","affiliation":[{"name":"Iowa State University, Ames, IA, USA"}]},{"given":"Murali","family":"Emani","sequence":"additional","affiliation":[{"name":"Argonne National Laboratory, Lemont, IL, USA"}]},{"given":"Venkatram","family":"Vishwanath","sequence":"additional","affiliation":[{"name":"Argonne National Laboratory, Lemont, IL, USA"}]},{"given":"Arun K.","family":"Somani","sequence":"additional","affiliation":[{"name":"Iowa State University, Ames, IA, USA"}]}],"member":"320","published-online":{"date-parts":[[2022,6,27]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/MM.2020.2971677"},{"key":"e_1_3_2_1_2_1","volume-title":"Proxylessnas: Direct neural architecture search on target task and hardware. arXiv preprint arXiv:1812.00332","author":"Cai Han","year":"2018","unstructured":"Han Cai , Ligeng Zhu , and Song Han . 2018 . Proxylessnas: Direct neural architecture search on target task and hardware. arXiv preprint arXiv:1812.00332 (2018). Han Cai, Ligeng Zhu, and Song Han. 2018. Proxylessnas: Direct neural architecture search on target task and hardware. arXiv preprint arXiv:1812.00332 (2018)."},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00242"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/ASAP49362.2020.00016"},{"volume-title":"Calibration Data-based CNN Filter Pruning for Efficient Layer Fusion. In 2020 IEEE 22nd International Conference on High Performance Computing and Communications","author":"Chitty-Venkata Krishna Teja","key":"e_1_3_2_1_5_1","unstructured":"Krishna Teja Chitty-Venkata and Arun K Somani . 2020. Calibration Data-based CNN Filter Pruning for Efficient Layer Fusion. In 2020 IEEE 22nd International Conference on High Performance Computing and Communications ; IEEE 18th International Conference on Smart City; IEEE 6th International Conference on Data Science and Systems (HPCC\/SmartCity\/DSS). IEEE , 1300--1307. Krishna Teja Chitty-Venkata and Arun K Somani. 2020. Calibration Data-based CNN Filter Pruning for Efficient Layer Fusion. In 2020 IEEE 22nd International Conference on High Performance Computing and Communications; IEEE 18th International Conference on Smart City; IEEE 6th International Conference on Data Science and Systems (HPCC\/SmartCity\/DSS). IEEE, 1300--1307."},{"key":"e_1_3_2_1_6_1","volume-title":"Array-Aware Neural Architecture Search. In 2021 IEEE 32nd International Conference on Application specific Systems, Architectures and Processors (ASAP). IEEE, 125--132","author":"Chitty-Venkata Krishna Teja","year":"2021","unstructured":"Krishna Teja Chitty-Venkata and Arun K Somani . 2021 . Array-Aware Neural Architecture Search. In 2021 IEEE 32nd International Conference on Application specific Systems, Architectures and Processors (ASAP). IEEE, 125--132 . Krishna Teja Chitty-Venkata and Arun K Somani. 2021. Array-Aware Neural Architecture Search. In 2021 IEEE 32nd International Conference on Application specific Systems, Architectures and Processors (ASAP). IEEE, 125--132."},{"key":"e_1_3_2_1_7_1","volume-title":"Neural Architecture Search Survey: A Hardware Perspective. ACM Computing Surveys (CSUR)","author":"Chitty-Venkata Krishna Teja","year":"2022","unstructured":"Krishna Teja Chitty-Venkata and Arun K Somani . 2022. Neural Architecture Search Survey: A Hardware Perspective. ACM Computing Surveys (CSUR) ( 2022 ). Krishna Teja Chitty-Venkata and Arun K Somani. 2022. Neural Architecture Search Survey: A Hardware Perspective. ACM Computing Surveys (CSUR) (2022)."},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/MM.2021.3061394"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"e_1_3_2_1_10_1","volume-title":"NeurIPS ML for Systems workshop","author":"Elthakeb Ahmed","year":"2019","unstructured":"Ahmed Elthakeb , Prannoy Pilligundla , FatemehSadat Mireshghallah , Amir Yazdanbakhsh , Sicuan Gao , and Hadi Esmaeilzadeh . 2019 . Releq: an automatic reinforcement learning approach for deep quantization of neural networks . In NeurIPS ML for Systems workshop , 2018. Ahmed Elthakeb, Prannoy Pilligundla, FatemehSadat Mireshghallah, Amir Yazdanbakhsh, Sicuan Gao, and Hadi Esmaeilzadeh. 2019. Releq: an automatic reinforcement learning approach for deep quantization of neural networks. In NeurIPS ML for Systems workshop, 2018."},{"key":"e_1_3_2_1_11_1","volume-title":"Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding. arXiv preprint arXiv:1510.00149","author":"Han Song","year":"2015","unstructured":"Song Han , Huizi Mao , and William J Dally . 2015. Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding. arXiv preprint arXiv:1510.00149 ( 2015 ). Song Han, Huizi Mao, and William J Dally. 2015. 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_12_1","volume-title":"Learning both weights and connections for efficient neural networks. arXiv preprint arXiv:1506.02626","author":"Han Song","year":"2015","unstructured":"Song Han , Jeff Pool , John Tran , and William J Dally . 2015. Learning both weights and connections for efficient neural networks. arXiv preprint arXiv:1506.02626 ( 2015 ). Song Han, Jeff Pool, John Tran, and William J Dally. 2015. Learning both weights and connections for efficient neural networks. arXiv preprint arXiv:1506.02626 (2015)."},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00140"},{"key":"e_1_3_2_1_15_1","volume-title":"Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861","author":"Howard Andrew G","year":"2017","unstructured":"Andrew G 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 preprint arXiv:1704.04861 (2017). Andrew G 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 preprint arXiv:1704.04861 (2017)."},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00286"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/MICRO.2016.7783722"},{"key":"e_1_3_2_1_18_1","volume-title":"Cutlass: Fast linear algebra in cuda c++. NVIDIA Developer Blog","author":"Kerr Andrew","year":"2017","unstructured":"Andrew Kerr , Duane Merrill , Julien Demouth , and John Tran . 2017 . Cutlass: Fast linear algebra in cuda c++. NVIDIA Developer Blog (2017). Andrew Kerr, Duane Merrill, Julien Demouth, and John Tran. 2017. Cutlass: Fast linear algebra in cuda c++. NVIDIA Developer Blog (2017)."},{"key":"e_1_3_2_1_19_1","volume-title":"Quantizing deep convolutional networks for efficient inference: A whitepaper. arXiv preprint arXiv:1806.08342","author":"Krishnamoorthi Raghuraman","year":"2018","unstructured":"Raghuraman Krishnamoorthi . 2018. Quantizing deep convolutional networks for efficient inference: A whitepaper. arXiv preprint arXiv:1806.08342 ( 2018 ). Raghuraman Krishnamoorthi. 2018. Quantizing deep convolutional networks for efficient inference: A whitepaper. arXiv preprint arXiv:1806.08342 (2018)."},{"key":"e_1_3_2_1_20_1","unstructured":"Alex Krizhevsky Geoffrey Hinton etal 2009. Learning multiple layers of features from tiny images. (2009).  Alex Krizhevsky Geoffrey Hinton et al. 2009. Learning multiple layers of features from tiny images. (2009)."},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW.2017.151"},{"key":"e_1_3_2_1_22_1","volume-title":"Darts: Differentiable architecture search. arXiv preprint arXiv:1806.09055","author":"Liu Hanxiao","year":"2018","unstructured":"Hanxiao Liu , Karen Simonyan , and Yiming Yang . 2018 . Darts: Differentiable architecture search. arXiv preprint arXiv:1806.09055 (2018). Hanxiao Liu, Karen Simonyan, and Yiming Yang. 2018. Darts: Differentiable architecture search. arXiv preprint arXiv:1806.09055 (2018)."},{"key":"e_1_3_2_1_23_1","volume-title":"Jeff Pool, Darko Stosic, Dusan Stosic, Ganesh Venkatesh, Chong Yu, and Paulius Micikevicius.","author":"Mishra Asit","year":"2021","unstructured":"Asit Mishra , Jorge Albericio Latorre , Jeff Pool, Darko Stosic, Dusan Stosic, Ganesh Venkatesh, Chong Yu, and Paulius Micikevicius. 2021 . Accelerating sparse deep neural networks. arXiv preprint arXiv:2104.08378 (2021). Asit Mishra, Jorge Albericio Latorre, Jeff Pool, Darko Stosic, Dusan Stosic, Ganesh Venkatesh, Chong Yu, and Paulius Micikevicius. 2021. Accelerating sparse deep neural networks. arXiv preprint arXiv:2104.08378 (2021)."},{"key":"e_1_3_2_1_24_1","volume-title":"Pytorch: An imperative style, high-performance deep learning library. Advances in neural information processing systems 32","author":"Paszke Adam","year":"2019","unstructured":"Adam Paszke , Sam Gross , Francisco Massa , Adam Lerer , James Bradbury , Gregory Chanan , Trevor Killeen , Zeming Lin , Natalia Gimelshein , Luca Antiga , 2019 . Pytorch: An imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019), 8026--8037. Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al. 2019. Pytorch: An imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019), 8026--8037."},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00474"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1109\/ISCA.2018.00069"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00293"},{"key":"e_1_3_2_1_28_1","volume-title":"International Conference on Machine Learning. PMLR, 6105--6114","author":"Tan Mingxing","year":"2019","unstructured":"Mingxing Tan and Quoc Le . 2019 . Efficientnet: Rethinking model scaling for convolutional neural networks . In International Conference on Machine Learning. PMLR, 6105--6114 . Mingxing Tan and Quoc Le. 2019. Efficientnet: Rethinking model scaling for convolutional neural networks. In International Conference on Machine Learning. PMLR, 6105--6114."},{"key":"e_1_3_2_1_29_1","volume-title":"Efficientnetv2: Smaller models and faster training. arXiv preprint arXiv:2104.00298","author":"Tan Mingxing","year":"2021","unstructured":"Mingxing Tan and Quoc V Le. 2021. Efficientnetv2: Smaller models and faster training. arXiv preprint arXiv:2104.00298 ( 2021 ). Mingxing Tan and Quoc V Le. 2021. Efficientnetv2: Smaller models and faster training. arXiv preprint arXiv:2104.00298 (2021)."},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1109\/FPL.2018.00059"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00881"},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.01099"},{"key":"e_1_3_2_1_33_1","volume-title":"Mixed precision quantization of convnets via differentiable neural architecture search. arXiv preprint arXiv:1812.00090","author":"Wu Bichen","year":"2018","unstructured":"Bichen Wu , Yanghan Wang , Peizhao Zhang , Yuandong Tian , Peter Vajda , and Kurt Keutzer . 2018. Mixed precision quantization of convnets via differentiable neural architecture search. arXiv preprint arXiv:1812.00090 ( 2018 ). Bichen Wu, Yanghan Wang, Peizhao Zhang, Yuandong Tian, Peter Vajda, and Kurt Keutzer. 2018. Mixed precision quantization of convnets via differentiable neural architecture search. arXiv preprint arXiv:1812.00090 (2018)."},{"key":"e_1_3_2_1_34_1","volume-title":"Releq: An automatic reinforcement learning approach for deep quantization of neural networks. arXiv preprint arXiv:1811.01704 1, 2","author":"Yazdanbakhsh Amir","year":"2018","unstructured":"Amir Yazdanbakhsh , Ahmed T Elthakeb , Prannoy Pilligundla , F Mireshghallah , and Hadi Esmaeilzadeh . 2018 . Releq: An automatic reinforcement learning approach for deep quantization of neural networks. arXiv preprint arXiv:1811.01704 1, 2 (2018). Amir Yazdanbakhsh, Ahmed T Elthakeb, Prannoy Pilligundla, F Mireshghallah, and Hadi Esmaeilzadeh. 2018. Releq: An automatic reinforcement learning approach for deep quantization of neural networks. arXiv preprint arXiv:1811.01704 1, 2 (2018)."},{"key":"e_1_3_2_1_35_1","volume-title":"Learning N: M fine-grained structured sparse neural networks from scratch. arXiv preprint arXiv:2102.04010","author":"Zhou Aojun","year":"2021","unstructured":"Aojun Zhou , Yukun Ma , Junnan Zhu , Jianbo Liu , Zhijie Zhang , Kun Yuan , Wenxiu Sun , and Hongsheng Li. 2021. Learning N: M fine-grained structured sparse neural networks from scratch. arXiv preprint arXiv:2102.04010 ( 2021 ). Aojun Zhou, Yukun Ma, Junnan Zhu, Jianbo Liu, Zhijie Zhang, Kun Yuan,Wenxiu Sun, and Hongsheng Li. 2021. Learning N: M fine-grained structured sparse neural networks from scratch. arXiv preprint arXiv:2102.04010 (2021)."},{"key":"e_1_3_2_1_36_1","volume-title":"Dorefa-net: Training low bitwidth convolutional neural networks with low bitwidth gradients. arXiv preprint arXiv:1606.06160","author":"Zhou Shuchang","year":"2016","unstructured":"Shuchang Zhou , Yuxin Wu , Zekun Ni , Xinyu Zhou , He Wen , and Yuheng Zou . 2016 . Dorefa-net: Training low bitwidth convolutional neural networks with low bitwidth gradients. arXiv preprint arXiv:1606.06160 (2016). Shuchang Zhou, Yuxin Wu, Zekun Ni, Xinyu Zhou, He Wen, and Yuheng Zou. 2016. Dorefa-net: Training low bitwidth convolutional neural networks with low bitwidth gradients. arXiv preprint arXiv:1606.06160 (2016)."},{"key":"e_1_3_2_1_37_1","volume-title":"Neural architecture search with reinforcement learning. arXiv preprint arXiv:1611.01578","author":"Zoph Barret","year":"2016","unstructured":"Barret Zoph and Quoc V Le. 2016. Neural architecture search with reinforcement learning. arXiv preprint arXiv:1611.01578 ( 2016 ). Barret Zoph and Quoc V Le. 2016. Neural architecture search with reinforcement learning. arXiv preprint arXiv:1611.01578 (2016)."}],"event":{"name":"HPDC '22: The 31st International Symposium on High-Performance Parallel and Distributed Computing","sponsor":["SIGHPC ACM Special Interest Group on High Performance Computing, Special Interest Group on High Performance Computing","SIGARCH ACM Special Interest Group on Computer Architecture"],"location":"Minneapolis MN USA","acronym":"HPDC '22"},"container-title":["Proceedings of the 31st International Symposium on High-Performance Parallel and Distributed Computing"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3502181.3531463","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/abs\/10.1145\/3502181.3531463","content-type":"text\/html","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3502181.3531463","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3502181.3531463","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T18:09:49Z","timestamp":1750183789000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3502181.3531463"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,27]]},"references-count":37,"alternative-id":["10.1145\/3502181.3531463","10.1145\/3502181"],"URL":"https:\/\/doi.org\/10.1145\/3502181.3531463","relation":{},"subject":[],"published":{"date-parts":[[2022,6,27]]},"assertion":[{"value":"2022-06-27","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}