{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,31]],"date-time":"2025-10-31T07:52:50Z","timestamp":1761897170066,"version":"3.37.3"},"reference-count":49,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100010002","name":"Ministry of Education","doi-asserted-by":"publisher","award":["NRF-2016R1D1A1B03934266"],"award-info":[{"award-number":["NRF-2016R1D1A1B03934266"]}],"id":[{"id":"10.13039\/100010002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["NRF-2019R1F1A1060959"],"award-info":[{"award-number":["NRF-2019R1F1A1060959"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2019]]},"DOI":"10.1109\/access.2019.2954546","type":"journal-article","created":{"date-parts":[[2019,11,20]],"date-time":"2019-11-20T21:02:29Z","timestamp":1574283749000},"page":"168048-168059","source":"Crossref","is-referenced-by-count":19,"title":["DMS: Dynamic Model Scaling for Quality-Aware Deep Learning Inference in Mobile and Embedded Devices"],"prefix":"10.1109","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4757-8999","authenticated-orcid":false,"given":"Woochul","family":"Kang","sequence":"first","affiliation":[]},{"given":"Daeyeon","family":"Kim","sequence":"additional","affiliation":[]},{"given":"Junyoung","family":"Park","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref39","first-page":"598","article-title":"Optimal brain damage","author":"lecun","year":"1990","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref38","article-title":"MobileNets: Efficient convolutional neural networks for mobile vision applications","author":"howard","year":"2017","journal-title":"arXiv 1704 04861"},{"key":"ref33","first-page":"215","article-title":"The ondemand governor","volume":"2","author":"pallipadi","year":"2006","journal-title":"Proc Linux Symp"},{"key":"ref32","first-page":"265","article-title":"TensorFlow: A system for large-scale machine learning","author":"abadi","year":"2016","journal-title":"Proc of USENIX Symp on Operating Systems Design and Implementation (OSDI)"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1007\/s11241-018-9314-y"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1002\/047166880X"},{"key":"ref37","article-title":"SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and ¡ 0.5 MB model size","author":"iandola","year":"2016","journal-title":"arXiv 1602 07360"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/CPSNA.2015.23"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/MC.2007.443"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1145\/2815400.2815403"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01234-2_48"},{"article-title":"Learning multiple layers of features from tiny images","year":"2009","author":"krizhevsky","key":"ref27"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2018.2887099"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1989.1.4.541"},{"journal-title":"Deep Learning","year":"2016","author":"goodfellow","key":"ref1"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/MPRV.2009.82"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1145\/3005348"},{"key":"ref21","first-page":"1","article-title":"Deep compression: Compressing deep neural networks with pruning, trained quantization and Huffman coding","author":"han","year":"2016","journal-title":"Proc Int Conf Learn Represent"},{"key":"ref24","first-page":"1","article-title":"Speeding up convolutional neural networks with low rank expansions","author":"jaderberg","year":"2014","journal-title":"Proc Brit Mach Vis Conf"},{"key":"ref23","doi-asserted-by":"crossref","first-page":"2365","DOI":"10.21437\/Interspeech.2013-552","article-title":"Restructuring of deep neural network acoustic models with singular value decomposition","author":"xue","year":"2013","journal-title":"Proc INTERSPEECH"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-015-0816-y"},{"key":"ref25","first-page":"1","article-title":"Very deep convolutional networks for large-scale image recognition","author":"simonyan","year":"2015","journal-title":"Proc Int Conf Learn Represent"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/IPSN.2016.7460664"},{"key":"ref11","first-page":"1269","article-title":"Exploiting linear structure within convolutional networks for efficient evaluation","author":"denton","year":"2014","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.298"},{"key":"ref12","first-page":"1135","article-title":"Learning both weights and connections for efficient neural network","author":"han","year":"2015","journal-title":"Proc 28th Int Conf Neural Inf Process Syst"},{"key":"ref13","first-page":"1","article-title":"Pruning filters for efficient convnets","author":"li","year":"2017","journal-title":"Proc Int Conf Learn Represent"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1145\/3007787.3001163"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1145\/2749469.2750389"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1145\/3079856.3080246"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-06486-4_7"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1145\/2647868.2654889"},{"journal-title":"Nvidia tensorrt","year":"2019","key":"ref19"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref3","first-page":"1097","article-title":"ImageNet classification with deep convolutional neural networks","author":"krizhevsky","year":"2012","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref5","first-page":"377","article-title":"Live video analytics at scale with approximation and delay-tolerance","author":"zhang","year":"2017","journal-title":"Proc USENIX Symp Netw Syst Design Implem (NSDI)"},{"journal-title":"This Powerful Wearable is a Life-Changer for the Blind","year":"2019","key":"ref8"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2014.81"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.23919\/DATE.2018.8342119"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/MS.2017.79"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01261-8_25"},{"key":"ref45","first-page":"2181","article-title":"Runtime neural pruning","author":"lin","year":"2017","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1145\/2893356"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1145\/3241539.3241559"},{"key":"ref42","first-page":"1","article-title":"Multi-scale dense networks for resource efficient image classification","author":"huang","year":"2018","journal-title":"Proc Int Conf Learn Represent"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/ICPR.2016.7900006"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2018.2878258"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.194"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/8600701\/08907822.pdf?arnumber=8907822","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,27]],"date-time":"2024-07-27T12:18:04Z","timestamp":1722082684000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/8907822\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"references-count":49,"URL":"https:\/\/doi.org\/10.1109\/access.2019.2954546","relation":{},"ISSN":["2169-3536"],"issn-type":[{"type":"electronic","value":"2169-3536"}],"subject":[],"published":{"date-parts":[[2019]]}}}