{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T13:27:50Z","timestamp":1790602070975,"version":"4.1.0"},"reference-count":41,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"1","license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Circuits Syst. I"],"published-print":{"date-parts":[[2021,1]]},"DOI":"10.1109\/tcsi.2020.3039116","type":"journal-article","created":{"date-parts":[[2020,12,1]],"date-time":"2020-12-01T22:38:06Z","timestamp":1606862286000},"page":"77-88","source":"Crossref","is-referenced-by-count":15,"title":["Fast and Accurate Inference on Microcontrollers With Boosted Cooperative Convolutional Neural Networks (BC-Net)"],"prefix":"10.1109","volume":"68","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6086-0606","authenticated-orcid":false,"given":"Luca","family":"Mocerino","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5881-3811","authenticated-orcid":false,"given":"Andrea","family":"Calimera","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1145\/2647868.2654889"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-42051-1_16"},{"key":"ref33","first-page":"6402","article-title":"Simple and scalable predictive uncertainty estimation using deep ensembles","author":"lakshminarayanan","year":"2017","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/ISVLSI49217.2020.000-5"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/CODESISSS.2018.8525915"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33013854"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.21437\/Interspeech.2017-1737"},{"key":"ref36","article-title":"Learning multiple layers of features from tiny images","author":"krizhevsky","year":"2009"},{"key":"ref35","first-page":"1861","article-title":"Probabilistic backpropagation for scalable learning of Bayesian neural networks","author":"hern\u00e1ndez-lobato","year":"2015","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00355"},{"key":"ref10","article-title":"Binarized neural networks: Training deep neural networks with weights and activations constrained to +1 or ?1","author":"courbariaux","year":"2016","journal-title":"arXiv 1602 02830 [cs]"},{"key":"ref11","first-page":"525","article-title":"XNOR-Net: ImageNet classification using binary convolutional neural networks","author":"rastegari","year":"2016","journal-title":"Vision Computer"},{"key":"ref40","article-title":"Very deep convolutional networks for large-scale image recognition","author":"simonyan","year":"2014","journal-title":"arXiv 1409 1556"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/AICAS48895.2020.9073982"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/CODESISSS.2015.7331375"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/ICECS46596.2019.8964993"},{"key":"ref15","first-page":"2849","article-title":"Fixed point quantization of deep convolutional networks","author":"lin","year":"2016","journal-title":"Proc Int Conf Mach Learn (ICML)"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1145\/2847263.2847265"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1145\/3240765.3240803"},{"key":"ref18","article-title":"Deep compression: Compressing deep neural networks with pruning, trained quantization and Huffman coding","author":"han","year":"2015","journal-title":"arXiv 1510 00149 [cs]"},{"key":"ref19","first-page":"3123","article-title":"Binaryconnect: Training deep neural networks with binary weights during propagations","author":"courbariaux","year":"2015","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref4","first-page":"4901","article-title":"Hybrid 8-bit floating point (HFP8) training and inference for deep neural networks","author":"sun","year":"2019","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/WACV.2018.00095"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2016.2579198"},{"key":"ref27","article-title":"Flexible deep neural network processing","author":"tann","year":"2018","journal-title":"arXiv 1801 07353"},{"key":"ref6","first-page":"6869","article-title":"Quantized neural networks: Training neural networks with low precision weights and activations","volume":"18","author":"hubara","year":"2017","journal-title":"J Mach Learn Res"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.23919\/DATE.2019.8714880"},{"key":"ref29","article-title":"Efficient hybrid network architectures for extremely quantized neural networks enabling intelligence at the edge","author":"chakraborty","year":"2019","journal-title":"arXiv 1902 00460"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2948577"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00821"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2017.2776930"},{"key":"ref9","article-title":"CMSIS-NN: Efficient neural network kernels for arm cortex-M CPUs","author":"lai","year":"2018","journal-title":"arXiv 1801 06601 [cs]"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1038\/nature14539"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/CoolChips.2018.8373076"},{"key":"ref21","article-title":"DoReFa-Net: Training low bitwidth convolutional neural networks with low bitwidth gradients","author":"zhou","year":"2016","journal-title":"arXiv 1606 06160 [cs]"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.23919\/FPL.2017.8056823"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/TCSI.2019.2907488"},{"key":"ref41","first-page":"1","article-title":"Adam: A method for stochastic optimization","author":"kingma","year":"2015","journal-title":"Proc Int Conf Learn Represent"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.23919\/DATE.2018.8342046"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1145\/3218603.3218611"}],"container-title":["IEEE Transactions on Circuits and Systems I: Regular Papers"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/8919\/9316996\/09275360.pdf?arnumber=9275360","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,1,12]],"date-time":"2022-01-12T16:03:37Z","timestamp":1642003417000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9275360\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,1]]},"references-count":41,"journal-issue":{"issue":"1"},"URL":"https:\/\/doi.org\/10.1109\/tcsi.2020.3039116","relation":{},"ISSN":["1549-8328","1558-0806"],"issn-type":[{"value":"1549-8328","type":"print"},{"value":"1558-0806","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,1]]}}}