{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T21:00:17Z","timestamp":1776114017088,"version":"3.50.1"},"reference-count":57,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"am","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["ECCS 1954589"],"award-info":[{"award-number":["ECCS 1954589"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["CCF 1815033"],"award-info":[{"award-number":["CCF 1815033"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Virginia Commonwealth Cyber Initiative (CCI) CCI Cybersecurity Research Collaboration Grant"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2022]]},"DOI":"10.1109\/access.2022.3196688","type":"journal-article","created":{"date-parts":[[2022,8,5]],"date-time":"2022-08-05T19:28:55Z","timestamp":1659727735000},"page":"84946-84959","source":"Crossref","is-referenced-by-count":25,"title":["Energy Efficient Learning With Low Resolution Stochastic Domain Wall Synapse for Deep Neural Networks"],"prefix":"10.1109","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4517-3330","authenticated-orcid":false,"given":"Walid Al","family":"Misba","sequence":"first","affiliation":[{"name":"Mechanical and Nuclear Engineering Department, Virginia Commonwealth University, Richmond, VA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mark","family":"Lozano","sequence":"additional","affiliation":[{"name":"Mechanical and Nuclear Engineering Department, Virginia Commonwealth University, Richmond, VA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Damien","family":"Querlioz","sequence":"additional","affiliation":[{"name":"CNRS, Centre de Nanosciences et de Nanotechnologies, Université Paris-Saclay, Palaiseau, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5681-0884","authenticated-orcid":false,"given":"Jayasimha","family":"Atulasimha","sequence":"additional","affiliation":[{"name":"Mechanical and Nuclear Engineering Department, Virginia Commonwealth University, Richmond, VA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1038\/s41928-018-0054-8"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.3389\/fnins.2020.00406"},{"key":"ref33","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":"ref32","first-page":"4035","article-title":"ZipML: Training linear models with end-to-end low precision, and a little bit of deep learning","volume":"70","author":"zhang","year":"2017","journal-title":"Proc 34th Int Conf Mach Learn"},{"key":"ref31","first-page":"3123","article-title":"Binaryconnect: Training deep neural networks with binary weights during propagations","volume":"2","author":"courbariaux","year":"2015","journal-title":"Proc 28th Int Conf Neural Inf Process Syst"},{"key":"ref30","first-page":"1","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":"ref37","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-018-0180-5"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-018-04933-y"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.23919\/VLSIT.2017.7998164"},{"key":"ref34","article-title":"Convolutional neural networks using logarithmic data representation","author":"miyashita","year":"2016","journal-title":"arXiv 1603 01025"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/TED.2021.3111846"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevLett.96.147204"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1063\/1.2976435"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/MDAT.2016.2573586"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1038\/nnano.2015.29"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/TNANO.2019.2916149"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1063\/5.0038521"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/TBCAS.2015.2414423"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1063\/1.5128344"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/TMAG.2018.2845890"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1038\/ncomms1024"},{"key":"ref25","first-page":"1","article-title":"Current-induced motion and pinning of domain walls in spin-valve nanowires studied by XMCD-PEEM","volume":"81","author":"uhl\u00ed?","year":"2010","journal-title":"Phys Rev B Condens Matter"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1038\/323533a0"},{"key":"ref51","article-title":"Quantization and training of neural networks for efficient integer-arithmetic-only inference","author":"jacob","year":"2017","journal-title":"arXiv 1712 05877"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1145\/3354265.3354267"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1038\/s41928-020-00523-3"},{"key":"ref55","doi-asserted-by":"crossref","DOI":"10.1063\/1.4838216","article-title":"A method to control magnetism in individual strain-mediated magnetoelectric islands","volume":"103","author":"cui","year":"2013","journal-title":"Appl Phys Lett"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.3389\/fnins.2019.01383"},{"key":"ref53","first-page":"63","article-title":"Reduction and IR-drop compensations techniques for reliable neuromorphic computing systems","author":"liu","year":"2015","journal-title":"Proc IEEE\/ACM Int Conf Comput -Aided Design (ICCAD)"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/AICAS.2019.8771544"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/TED.2015.2439635"},{"key":"ref11","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1038\/nature14441","article-title":"Training and operation of an integrated neuromorphic network based on metal-oxide memristors","volume":"521","author":"prezioso","year":"2015","journal-title":"Nature"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-020-16108-9"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/IEDM.2011.6131488"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1002\/adma.201402696"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/TED.2011.2147791"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/LED.2016.2582859"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-018-04484-2"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1038\/ncomms15199"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1016\/j.jmmm.2019.165434"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/TBCAS.2016.2525823"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2021.3113872"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2019.2954503"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1038\/s41928-018-0092-2"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1038\/s41565-020-0655-z"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1038\/s41928-017-0002-z"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1145\/2897937.2898010"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"ref9","first-page":"89","article-title":"Neuromorphic computing using non-volatile memory","volume":"2","author":"burr","year":"2016","journal-title":"Adv Phys X"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1063\/1.4937557"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevB.75.174409"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1063\/5.0046032"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1063\/1.4899186"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.23919\/DATE.2017.7926952"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.23919\/Eusipco47968.2020.9287574"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-020-1942-4"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1145\/2744769.2744930"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"https:\/\/ieeexplore.ieee.org\/ielam\/6287639\/9668973\/9850990-aam.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/9668973\/09850990.pdf?arnumber=9850990","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,25]],"date-time":"2023-11-25T10:24:05Z","timestamp":1700907845000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9850990\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"references-count":57,"URL":"https:\/\/doi.org\/10.1109\/access.2022.3196688","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]}}}