{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T05:31:00Z","timestamp":1782970260518,"version":"3.54.5"},"reference-count":57,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"4","license":[{"start":{"date-parts":[[2020,5,1]],"date-time":"2020-05-01T00:00:00Z","timestamp":1588291200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2020,5,1]],"date-time":"2020-05-01T00:00:00Z","timestamp":1588291200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2020,5,1]],"date-time":"2020-05-01T00:00:00Z","timestamp":1588291200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"Infosys Center for AI at IIIT-Delhi and Microsoft Research, India"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE J. Sel. Top. Signal Process."],"published-print":{"date-parts":[[2020,5]]},"DOI":"10.1109\/jstsp.2020.2968810","type":"journal-article","created":{"date-parts":[[2020,1,22]],"date-time":"2020-01-22T22:03:08Z","timestamp":1579730588000},"page":"737-749","source":"Crossref","is-referenced-by-count":15,"title":["Symmetric $k$-Means for Deep Neural Network Compression and Hardware Acceleration on FPGAs"],"prefix":"10.1109","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0243-9810","authenticated-orcid":false,"given":"Akshay","family":"Jain","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pulkit","family":"Goel","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shivam","family":"Aggarwal","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9955-2643","authenticated-orcid":false,"given":"Alexander","family":"Fell","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Saket","family":"Anand","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.23919\/DATE.2017.7927162"},{"key":"ref38","first-page":"815","article-title":"AMC: AutoML for model compression and acceleration on mobile devices","author":"he","year":"0","journal-title":"Proc Eur Conf Comput Vision"},{"key":"ref33","article-title":"Ternary hybrid neural-tree networks for highly constrained IoT applications","author":"gope","year":"0","journal-title":"Proc Conf Machine Learning and Systems"},{"key":"ref32","first-page":"4992","article-title":"StrassenNets: Deep learning with a multiplication budget","author":"tschannen","year":"0","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref31","article-title":"Trained ternary quantization","author":"zhu","year":"0","journal-title":"Int Conf Learn Representations"},{"key":"ref30","article-title":"Ternary weight networks","author":"li","year":"2016","journal-title":"1st Int Workshop Efficient Methods for Deep Neural Networks"},{"key":"ref37","first-page":"1935","article-title":"Resource-efficient machine learning in 2 kb ram for the internet of things","author":"kumar","year":"0","journal-title":"Proc 34th Int Conf Mach Learn -Vol 70"},{"key":"ref36","article-title":"Distilling the knowledge in a neural network","author":"hinton","year":"2014","journal-title":"NIPS Deep Learning Workshop arXiv 1503 02531"},{"key":"ref35","article-title":"Mobilenets: Efficient convolutional neural networks for mobile vision applications","author":"howard","year":"2017","journal-title":"arXiv 1704 04861"},{"key":"ref34","article-title":"Ternary mobilenets via per-layer hybrid filter banks","author":"gope","year":"2019","journal-title":"arXiv 1911 01028"},{"key":"ref28","first-page":"4107","article-title":"Binarized neural networks","author":"hubara","year":"2016","journal-title":"Advances in Neural IInformation Processing Systems"},{"key":"ref27","first-page":"3123","article-title":"BinaryConnect: Training Deep Neural Networks with binary weights during propagations","author":"courbariaux","year":"2015","journal-title":"Advances in Neural IInformation Processing Systems"},{"key":"ref29","first-page":"525","article-title":"XNOR-Net: ImageNet classification using binary convolutional neural networks","author":"rastegari","year":"0","journal-title":"Proc Eur Conf Comput Vision"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/JETCAS.2019.2911899"},{"key":"ref1","doi-asserted-by":"crossref","DOI":"10.1038\/nature14539","article-title":"Deep Learning","volume":"521","author":"lecun","year":"2015","journal-title":"Nature"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/EMC249363.2019.00013"},{"key":"ref22","first-page":"395","article-title":"C ir cnn: accelerating and compressing deep neural networks using block-circulant weight matrices","author":"ding","year":"0","journal-title":"Proc Annu IEEE\/ACM Int Symp Microarchitecture"},{"key":"ref21","article-title":"Towards the limit of network quantization","author":"choi","year":"0","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref24","article-title":"Compressing RNNs for IoT devices by 15&#x2013;38x using Kronecker products","author":"thakker","year":"2019","journal-title":"arXiv 1906 02876"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1145\/3174243.3174253"},{"key":"ref26","first-page":"2849","article-title":"Fixed point quantization of deep convolutional networks","author":"lin","year":"0","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref25","first-page":"1737","article-title":"Deep learning with limited numerical precision","author":"gupta","year":"0","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref50","article-title":"Bnn+: Improved binary network training","author":"darabi","year":"2018","journal-title":"arXiv 1812 11800"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298925"},{"key":"ref56","first-page":"4040","article-title":"A large dataset to train convolutional networks for disparity, optical flow, and scene flow estimation","author":"mayer","year":"0","journal-title":"Proc IEEE Conf Comput Vision Pattern Recognit"},{"key":"ref55","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1023\/A:1014573219977","article-title":"A taxonomy and evaluation of dense Two-Frame stereo correspondence algorithms","volume":"47","author":"scharstein","year":"2002","journal-title":"Int J Comput Vision"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-014-0733-5"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00716"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00474"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1145\/3020078.3021736"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/ASPDAC.2016.7428073"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1145\/2847263.2847276"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1145\/2847263.2847265"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1145\/2684746.2689060"},{"key":"ref14","first-page":"1106","article-title":"ImageNet classification with deep convolutional neural networks","author":"krizhevsky","year":"2012","journal-title":"Advances in Neural Information Processing Systems 25"},{"key":"ref15","article-title":"Very deep convolutional networks for large-scale image recognition","author":"simonyan","year":"0","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref16","first-page":"6775","article-title":"Structured bayesian pruning via log-normal multiplicative noise","author":"neklyudov","year":"0","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref17","article-title":"To prune, or not to prune: Exploring the efficacy of pruning for model compression","author":"zhu","year":"0","journal-title":"Proc Intl Conf on Learning Representations"},{"key":"ref18","article-title":"Compressing deep convolutional networks using vector quantization","author":"gong","year":"2014","journal-title":"arXiv 1412 6115"},{"key":"ref19","article-title":"Factorization tricks for lstm networks","author":"kuchaiev","year":"0","journal-title":"Proc Workshop Int Conf Learn Represent"},{"key":"ref4","first-page":"1135","article-title":"Learning both weights and connections for efficient neural network","author":"han","year":"0","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref3","article-title":"Deep compression: compressing deep neural network with pruning, trained quantization and huffman coding","author":"han","year":"0","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref6","article-title":"Incremental network quantization: Towards lossless cnns with low-precision weights","author":"zhou","year":"0","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref5","first-page":"2285","article-title":"Compressing neural networks with the hashing trick","author":"chen","year":"0","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1145\/3007787.3001163"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1145\/2749469.2750389"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1145\/3007787.3001177"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00567"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.690"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2012.59"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1145\/3061639.3062259"},{"key":"ref44","article-title":"Learning multiple layers of features from tiny images","author":"krizhevsky","year":"2009"},{"key":"ref43","first-page":"8024","article-title":"PyTorch: An imperative style, high-performance deep learning library","author":"paszke","year":"0","journal-title":"Advances in Neural IInformation Processing Systems"}],"container-title":["IEEE Journal of Selected Topics in Signal Processing"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/4200690\/9163420\/08966322.pdf?arnumber=8966322","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,4,27]],"date-time":"2022-04-27T17:08:25Z","timestamp":1651079305000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/8966322\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,5]]},"references-count":57,"journal-issue":{"issue":"4"},"URL":"https:\/\/doi.org\/10.1109\/jstsp.2020.2968810","relation":{},"ISSN":["1932-4553","1941-0484"],"issn-type":[{"value":"1932-4553","type":"print"},{"value":"1941-0484","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,5]]}}}