{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,27]],"date-time":"2026-06-27T03:14:23Z","timestamp":1782530063626,"version":"3.54.5"},"reference-count":40,"publisher":"IEEE","license":[{"start":{"date-parts":[[2021,4,7]],"date-time":"2021-04-07T00:00:00Z","timestamp":1617753600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2021,4,7]],"date-time":"2021-04-07T00:00:00Z","timestamp":1617753600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,4,7]],"date-time":"2021-04-07T00:00:00Z","timestamp":1617753600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,4,7]]},"DOI":"10.1109\/isqed51717.2021.9424332","type":"proceedings-article","created":{"date-parts":[[2021,5,11]],"date-time":"2021-05-11T00:09:51Z","timestamp":1620691791000},"page":"135-141","source":"Crossref","is-referenced-by-count":32,"title":["Improving DNN Fault Tolerance using Weight Pruning and Differential Crossbar Mapping for ReRAM-based Edge AI"],"prefix":"10.1109","author":[{"given":"Geng","family":"Yuan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhiheng","family":"Liao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaolong","family":"Ma","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuxuan","family":"Cai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhenglun","family":"Kong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xuan","family":"Shen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingyan","family":"Fu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhengang","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chengming","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongwu","family":"Peng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ning","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ao","family":"Ren","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinhui","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanzhi","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2019.2899262"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-020-62676-7"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/ASP-DAC47756.2020.9045658"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/ISLPED.2019.8824944"},{"key":"ref31","article-title":"Non-structured dnn weight pruning–is it beneficial in any platform?","author":"ma","year":"2019","journal-title":"arXiv preprint arXiv 1907 09977"},{"key":"ref30","article-title":"6.7 ms on mobile with over 78% imagenet accuracy: Unified network pruning and architecture search for beyond real-time mobile acceleration","author":"li","year":"2020","journal-title":"2012 arXiv preprint arXiv"},{"key":"ref37","article-title":"The lottery ticket hypothesis: Finding sparse, trainable neural networks","author":"frankle","year":"2018","journal-title":"ICLRE"},{"key":"ref36","article-title":"Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding","author":"han","year":"2015","journal-title":"arXiv preprint arXiv 1510 00149"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.155"},{"key":"ref34","article-title":"Learning structured sparsity in deep neural networks","author":"wen","year":"2016","journal-title":"NeurIPS"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/TCT.1971.1083337"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01237-3_12"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/MWSCAS.2017.8053125"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1145\/3123939.3124552"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN48605.2020.9206751"},{"key":"ref14","article-title":"Towards ultra-high performance and energy efficiency of deep learning systems: an algorithm-hardware co-optimization framework","author":"wang","year":"2018","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/ISQED.2019.8697495"},{"key":"ref16","article-title":"An area and energy efficient design of domain-wall memory-based deep convolutional neural networks using stochastic computing","year":"2018","journal-title":"19th ISQED"},{"key":"ref17","article-title":"Real-time mobile acceleration of dnns: From computer vision to medical applications","author":"li","year":"2021","journal-title":"Proc Asia and South Pacific Design Automation Conference"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2018.8489619"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2020\/388"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2020.2976475"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.23919\/DATE.2017.7926952"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1145\/1785481.1785548"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1145\/3400302.3418782"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1145\/3061639.3062248"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/NANOARCH47378.2019.181304"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1145\/3061639.3062310"},{"key":"ref8","author":"ren","year":"2018","journal-title":"Admm-nn An algorithm-hardware co-design framework of dnns using alternating direction method of multipliers"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/TCAD.2019.2944582"},{"key":"ref2","article-title":"Bert: Pre-training of deep bidirectional transformers for language understanding","author":"devlin","year":"2019","journal-title":"Proceedings of NAACL-HLT"},{"key":"ref9","article-title":"Rram defect modeling and failure analysis based on march test and a novel squeeze-search scheme","author":"chen","year":"2015","journal-title":"IEEE Transactions on Computers"},{"key":"ref1","author":"goodfellow","year":"2016","journal-title":"Deep Learning"},{"key":"ref20","article-title":"Encoding, model, and architecture: Systematic optimization for spiking neural network in fpgas","author":"fang","year":"2020","journal-title":"2020 IEEE\/ACM International Conference On Computer Aided Design (ICCAD)"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1145\/3007787.3001139"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1145\/3007787.3001140"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/TCAD.2015.2394434"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/ISCA45697.2020.00073"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/NANOARCH.2015.7180600"},{"key":"ref25","article-title":"Rram defect modeling and failure analysis based on march test and a novel squeeze-search scheme","author":"chen","year":"0","journal-title":"IEEE Transactions on Computers"}],"event":{"name":"2021 22nd International Symposium on Quality Electronic Design (ISQED)","location":"Santa Clara, CA, USA","start":{"date-parts":[[2021,4,7]]},"end":{"date-parts":[[2021,4,9]]}},"container-title":["2021 22nd International Symposium on Quality Electronic Design (ISQED)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9424228\/9424248\/09424332.pdf?arnumber=9424332","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,10]],"date-time":"2022-05-10T15:41:23Z","timestamp":1652197283000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9424332\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,4,7]]},"references-count":40,"URL":"https:\/\/doi.org\/10.1109\/isqed51717.2021.9424332","relation":{},"subject":[],"published":{"date-parts":[[2021,4,7]]}}}