{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,11]],"date-time":"2026-07-11T15:42:11Z","timestamp":1783784531169,"version":"3.55.0"},"reference-count":21,"publisher":"IEEE","license":[{"start":{"date-parts":[[2021,10,6]],"date-time":"2021-10-06T00:00:00Z","timestamp":1633478400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2021,10,6]],"date-time":"2021-10-06T00:00:00Z","timestamp":1633478400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,10,6]],"date-time":"2021-10-06T00:00:00Z","timestamp":1633478400000},"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":[[2021,10,6]]},"DOI":"10.1109\/dft52944.2021.9568363","type":"proceedings-article","created":{"date-parts":[[2021,10,20]],"date-time":"2021-10-20T19:30:07Z","timestamp":1634758207000},"page":"1-6","source":"Crossref","is-referenced-by-count":25,"title":["Zero-Overhead Protection for CNN Weights"],"prefix":"10.1109","author":[{"given":"Stephane","family":"Burel","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Adrian","family":"Evans","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lorena","family":"Anghel","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref10","first-page":"367","author":"chen","year":"2016","journal-title":"Eyeriss A Spatial Architecture for Energy-Efficient Dataflow for Convolutional Neural Networks"},{"key":"ref11","first-page":"92","author":"du","year":"2015","journal-title":"Shidiannao shifting vision processing closer to the sensor"},{"key":"ref12","article-title":"3rd generation Intel Xeon Scalable Processors","year":"0"},{"key":"ref13","author":"kalamkar","year":"2019","journal-title":"A study of BFLOAT16 for Deep Learning Training"},{"key":"ref14","author":"micikevicius","year":"2018","journal-title":"Mixed Precision Training"},{"key":"ref15","article-title":"ONXX","year":"0"},{"key":"ref16","article-title":"N2D2","author":"bichler","year":"0"},{"key":"ref17","first-page":"1325","author":"nagel","year":"2019","journal-title":"Data-free quantization through weight equalization and bias correction"},{"key":"ref18","first-page":"1","author":"kim","year":"2018","journal-title":"MATIC Learning around errors for efficient low-voltage neural network accelerators"},{"key":"ref19","first-page":"310","author":"draghetti","year":"2019","journal-title":"Detecting Errors in Convolutional Neural Networks Using Inter Frame Spatio- Temporal Correlation"},{"key":"ref4","first-page":"1","author":"li","year":"2017","journal-title":"Understanding error propagation in deep learning neural network (DNN) accelerators and applications"},{"key":"ref3","first-page":"1","author":"dos santos","year":"2019","journal-title":"Impact of Reduced Precision in the Reliability of Deep Neural Networks for Object Detection"},{"key":"ref6","first-page":"267","author":"reagen","year":"2016","journal-title":"Minerva Enabling low-power highly-accurate deep neural network accelerators ISCA"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/VTS.2018.8368656"},{"key":"ref8","article-title":"SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and< 0.5 MB model size","author":"iandola","year":"2016","journal-title":"ArXiv Preprint"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/ICESS.2019.8782505"},{"key":"ref1","first-page":"1507","author":"schorn","year":"2019","journal-title":"An Efficient Bit-Flip Resilience Optimization Method for Deep Neural Networks"},{"key":"ref9","author":"howard","year":"2017","journal-title":"Mobilenets Efficient convolutional neural networks for mobile vision applications"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/DFT.2019.8875314"},{"key":"ref21","first-page":"56","article-title":"4.2 a 12nm autonomous-driving processor with 60.4tops, 13.8tops\/w cnn executed by task-separated asil d control","volume":"64","author":"matsubara","year":"0","journal-title":"2021 IEEE International Solid-State Circuits Conference-(ISSCC)"}],"event":{"name":"2021 IEEE International Symposium on Defect and Fault Tolerance in VLSI and Nanotechnology Systems (DFT)","location":"Athens, Greece","start":{"date-parts":[[2021,10,6]]},"end":{"date-parts":[[2021,10,8]]}},"container-title":["2021 IEEE International Symposium on Defect and Fault Tolerance in VLSI and Nanotechnology Systems (DFT)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9568273\/9568279\/09568363.pdf?arnumber=9568363","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,10]],"date-time":"2022-05-10T15:47:21Z","timestamp":1652197641000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9568363\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10,6]]},"references-count":21,"URL":"https:\/\/doi.org\/10.1109\/dft52944.2021.9568363","relation":{},"subject":[],"published":{"date-parts":[[2021,10,6]]}}}