{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,5]],"date-time":"2026-09-05T09:38:56Z","timestamp":1788601136288,"version":"build-2803163510"},"reference-count":59,"publisher":"IEEE","license":[{"start":{"date-parts":[[2020,6,1]],"date-time":"2020-06-01T00:00:00Z","timestamp":1590969600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2020,6,1]],"date-time":"2020-06-01T00:00:00Z","timestamp":1590969600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2020,6,1]],"date-time":"2020-06-01T00:00:00Z","timestamp":1590969600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,6]]},"DOI":"10.1109\/wf-iot48130.2020.9221198","type":"proceedings-article","created":{"date-parts":[[2020,10,13]],"date-time":"2020-10-13T20:15:07Z","timestamp":1602620107000},"page":"1-6","source":"Crossref","is-referenced-by-count":85,"title":["A Survey of Methods for Low-Power Deep Learning and Computer Vision"],"prefix":"10.1109","author":[{"given":"Abhinav","family":"Goel","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Caleb","family":"Tung","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yung-Hsiang","family":"Lu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"George K.","family":"Thiruvathukal","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref39","article-title":"Multi-Bias Non-linear Activation in Deep Neural Networks","author":"li","year":"2016"},{"key":"ref38","article-title":"Attention Based Pruning for Shift Networks","author":"hacene","year":"2019"},{"key":"ref33","first-page":"2074","article-title":"Learning Structured Sparsity in Deep Neural Networks","author":"wen","year":"0","journal-title":"NeurIPS 2016"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46493-0_40"},{"key":"ref31","article-title":"Pruning Filters for Efficient ConvNets","author":"li","year":"2016"},{"key":"ref30","article-title":"Fast and Balanced: Efficient Label Tree Learning for Large Scale Object Recognition","author":"deng","year":"0","journal-title":"2011 NeurIPS"},{"key":"ref37","article-title":"Shift: A Zero FLOP, Zero Parameter Alternative to Spatial Convolutions","author":"wu","year":"2017"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00140"},{"key":"ref35","article-title":"SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size","author":"iandola","year":"2016"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1145\/3065386"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00958"},{"key":"ref27","first-page":"1135","article-title":"Learning both Weights and Connections for Efficient Neural Network","author":"han","year":"0","journal-title":"NeurIPS 2015"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1145\/3240765.3240845"},{"key":"ref2","article-title":"Recent progress in semantic image segmentation","author":"liu","year":"2018"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2018.2876865"},{"key":"ref20","article-title":"Compressing Neural Networks with the Hashing Trick","author":"chen","year":"2015"},{"key":"ref22","article-title":"Towards the Limit of Network Quantization","author":"choi","year":"2017"},{"key":"ref21","article-title":"Deep Neural Networks are Robust to Weight Binarization and Other Non-Linear Distortions","author":"merolla","year":"2016"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/ICNN.1993.298572"},{"key":"ref23","article-title":"Optimal Brain Damage","author":"lecun","year":"0","journal-title":"1990 NeurIPS"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2015.2494536"},{"key":"ref25","article-title":"Structured Pruning of Deep Convolutional Neural Networks","author":"anwar","year":"2015"},{"key":"ref50","article-title":"ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware","author":"cai","year":"2019"},{"key":"ref51","article-title":"Single-Path NAS: Designing Hardware-Efficient ConvNets in less than 4 Hours","author":"stamoulis","year":"2019"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00511"},{"key":"ref58","article-title":"FitNets: Hints for Thin Deep Nets","author":"romero","year":"2015"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.776"},{"key":"ref56","article-title":"Distilling the Knowledge in a Neural Network","author":"hinton","year":"2015"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00489"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1145\/1150402.1150464"},{"key":"ref53","article-title":"Do Deep Nets Really Need to be Deep?","author":"ba","year":"0","journal-title":"2014 NeurIPS"},{"key":"ref52","article-title":"Representation Learning: A Review and New Perspectives","author":"bengio","year":"2014"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/JETCAS.2019.2911899"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1145\/3316781.3317828"},{"key":"ref40","article-title":"ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices","author":"zhang","year":"2017"},{"key":"ref12","first-page":"7675","article-title":"Training Deep Neural Networks with 8-bit Floating Point Numbers","author":"wang","year":"0","journal-title":"NeurIPS 2018"},{"key":"ref13","article-title":"Training Deep Neural Networks with Low Precision Multiplications","author":"courbariaux","year":"2015"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1145\/3060403.3060465"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/BigData.2018.8622329"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/ACSSC.2017.8335699"},{"key":"ref17","article-title":"Binarized Neural Networks: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1","author":"courbariaux","year":"2016"},{"key":"ref18","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-319-46493-0_32","article-title":"XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks","author":"rastegari","year":"2016"},{"key":"ref19","article-title":"DoReFa-Net: Training Low Bitwidth Convo-lutional Neural Networks with Low Bitwidth Gradients","author":"zhou","year":"2018"},{"key":"ref4","article-title":"Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding","author":"han","year":"2016"},{"key":"ref3","article-title":"Very Deep Convolutional Networks for Large-Scale Image Recognition","author":"simonyan","year":"0"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/TVLSI.2019.2905242"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/MC.2010.98"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/ISCAS.2017.8050296"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.01099"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/TENCON.2017.8228008"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00907"},{"key":"ref45","article-title":"Convolutional Neural Networks with Low-Rank Regularization","author":"tai","year":"2016"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00293"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33014780"},{"key":"ref42","article-title":"Exploiting Linear Structure Within Convolutional Networks for Efficient Evaluation","author":"denton","year":"0","journal-title":"2014 NeurIPS"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.5244\/C.28.88"},{"key":"ref44","first-page":"5553","article-title":"Exploring Unexplored Tensor Network Decompositions for Convolutional Neural Networks","author":"hayashi","year":"0","journal-title":"NeurIPS 2019"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1137\/07070111X"}],"event":{"name":"2020 IEEE 6th World Forum on Internet of Things (WF-IoT)","location":"New Orleans, LA, USA","start":{"date-parts":[[2020,6,2]]},"end":{"date-parts":[[2020,6,16]]}},"container-title":["2020 IEEE 6th World Forum on Internet of Things (WF-IoT)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9217527\/9221008\/09221198.pdf?arnumber=9221198","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,6,30]],"date-time":"2022-06-30T15:17:03Z","timestamp":1656602223000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9221198\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,6]]},"references-count":59,"URL":"https:\/\/doi.org\/10.1109\/wf-iot48130.2020.9221198","relation":{},"subject":[],"published":{"date-parts":[[2020,6]]}}}