{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,28]],"date-time":"2026-10-28T10:57:40Z","timestamp":1793185060316,"version":"4.5.0"},"publisher-location":"New York, NY, USA","reference-count":19,"publisher":"ACM","license":[{"start":{"date-parts":[[2021,1,18]],"date-time":"2021-01-18T00:00:00Z","timestamp":1610928000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"NSF","award":["1814920"],"award-info":[{"award-number":["1814920"]}]},{"name":"DoD ARO","award":["W911NF-19-1-0484"],"award-info":[{"award-number":["W911NF-19-1-0484"]}]},{"name":"VR","award":["2016-05140"],"award-info":[{"award-number":["2016-05140"]}]},{"name":"STINT","award":["MG2018-8007"],"award-info":[{"award-number":["MG2018-8007"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2021,1,18]]},"DOI":"10.1145\/3394885.3431535","type":"proceedings-article","created":{"date-parts":[[2021,1,29]],"date-time":"2021-01-29T06:32:48Z","timestamp":1611901968000},"page":"819-824","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":19,"title":["BatchSizer"],"prefix":"10.1145","author":[{"given":"Seyed Morteza","family":"Nabavinejad","sequence":"first","affiliation":[{"name":"Institute for Research in Fundamental Sciences (IPM), Tehran, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sherief","family":"Reda","sequence":"additional","affiliation":[{"name":"Brown University, Providence, RI"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Masoumeh","family":"Ebrahimi","sequence":"additional","affiliation":[{"name":"KTH Royal Institute of Technology, Stockholm, Sweden"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2021,1,29]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Retrieved","author":"NVIDIA Corporation","year":"2016"},{"key":"e_1_3_2_1_2_1","volume-title":"The caltech-256: Caltech technical report. vol 7694","author":"Griffin G","year":"2007"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.parco.2018.02.001"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46493-0_38"},{"key":"e_1_3_2_1_5_1","unstructured":"Andrew Howard Andrey Zhmoginov Liang-Chieh Chen Mark Sandler and Menglong Zhu. 2018. Inverted residuals and linear bottlenecks: Mobile networks for classification detection and segmentation. (2018).  Andrew Howard Andrey Zhmoginov Liang-Chieh Chen Mark Sandler and Menglong Zhu. 2018. Inverted residuals and linear bottlenecks: Mobile networks for classification detection and segmentation. (2018)."},{"key":"e_1_3_2_1_6_1","volume-title":"Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861","author":"Howard Andrew G","year":"2017"},{"key":"e_1_3_2_1_7_1","volume-title":"Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:1502.03167","author":"Ioffe Sergey","year":"2015"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/CGO.2015.7054182"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCD.2013.6657064"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01246-5_2"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/LCA.2019.2942020"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"crossref","unstructured":"Olga Russakovsky Jia Deng Hao Su Jonathan Krause Sanjeev Satheesh Sean Ma Zhiheng Huang Andrej Karpathy Aditya Khosla Michael Bernstein etal 2015. Imagenet large scale visual recognition challenge. International journal of computer vision 115 3 (2015) 211--252.  Olga Russakovsky Jia Deng Hao Su Jonathan Krause Sanjeev Satheesh Sean Ma Zhiheng Huang Andrej Karpathy Aditya Khosla Michael Bernstein et al. 2015. Imagenet large scale visual recognition challenge. International journal of computer vision 115 3 (2015) 211--252.","DOI":"10.1007\/s11263-015-0816-y"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2013.6639347"},{"key":"e_1_3_2_1_14_1","unstructured":"Ilya Sutskever Oriol Vinyals and Quoc V Le. 2014. Sequence to sequence learning with neural networks. In Advances in neural info. processing sys. 3104--3112.  Ilya Sutskever Oriol Vinyals and Quoc V Le. 2014. Sequence to sequence learning with neural networks. In Advances in neural info. processing sys. 3104--3112."},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.308"},{"key":"e_1_3_2_1_17_1","unstructured":"Zhenheng Tang et al. 2019. The impact of GPU DVFS on the energy and performance of deep learning: An empirical study. In e-Energy '19. 315--325.  Zhenheng Tang et al. 2019. The impact of GPU DVFS on the energy and performance of deep learning: An empirical study. In e-Energy '19. 315--325."},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"crossref","unstructured":"Hadi Zamani et al. 2019. GreenMM: energy efficient GPU matrix multiplication through undervolting. In ICS. 308--318.  Hadi Zamani et al. 2019. GreenMM: energy efficient GPU matrix multiplication through undervolting. In ICS. 308--318.","DOI":"10.1145\/3330345.3330373"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00907"}],"event":{"name":"ASPDAC '21: 26th Asia and South Pacific Design Automation Conference","location":"Tokyo Japan","acronym":"ASPDAC '21","sponsor":["SIGDA ACM Special Interest Group on Design Automation","IEEE CAS","IEEE CEDA"]},"container-title":["Proceedings of the 26th Asia and South Pacific Design Automation Conference"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3394885.3431535","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3394885.3431535","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3394885.3431535","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T17:32:02Z","timestamp":1750181522000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3394885.3431535"}},"subtitle":["Power-Performance Trade-off for DNN Inference"],"short-title":[],"issued":{"date-parts":[[2021,1,18]]},"references-count":19,"alternative-id":["10.1145\/3394885.3431535","10.1145\/3394885"],"URL":"https:\/\/doi.org\/10.1145\/3394885.3431535","relation":{},"subject":[],"published":{"date-parts":[[2021,1,18]]},"assertion":[{"value":"2021-01-29","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}