{“状态”:“确定”,“消息类型”:“工作”,“信息版本”:“1.0.0”,“讯息”:{“索引”:{“日期部分”:[[2022,12,13]],“日期时间”:“2022-12-13T18:51:09Z”,“时间戳”:1670957469213},“参考计数”:69,“出版商”:“电气与电子工程师学会(IEEE)”,“发行”:“4”,“许可证”:[{“开始”:{-“日期部分“:[2018,1]],”时间”:“2018-10-01T00:00:00Z”,“timestamp”:1538352000000},“content-version”:“vor”,“delay-in-days”:0,“URL”:“https:\/\/ieeexplore.iee.org\/Xplorehelp\/downloads\/license-information\/ieee.html”},{“start”:{“date-parts”:[[2018,10,1]],“date-time”:“2018-10-01T00:00:00Z”“:”am“,”delay-in-days“:0,”URL“:“https:\/\/ieeexplore.iee.org\/Xplorehelp\/downloads\/license-information\/ieee.html”},{“开始”:{“日期-部分”:[[2018,10,1]],“日期-时间”:“2018-10-01T00:00:00Z”,“时间戳”:1538352000000},“内容-版本”:“stm-asf”,“延迟-天”:0,“URL”:“http:\\/doi.org\/10.15223\/policy-029”}“:{”日期部分“:[[2018,10,1]],”日期时间“:“2018-10-01T00:00:00Z”,“timestamp”:1538352000000},“content-version”:“stm-asf”,“delay-in-days”:0,“URL”:“https:\/\/doi.org\/10.15223\/policy-037”}],“funder”:[{“name”:“Spintronic Materials,Interfaces,and Novel Architectures中心”},{“doi”:“10.13039\/1000000028”,“name”:“Semiconductor Research Corporation”,“doi-asserted-by”:“publisher”},{“doi”:“10.13039\/501000008982”,“name”:“国家科学基金会”,“doi-asserted-by”:“publisher”},{“doi”:“10.130.39\/100002418”,“name”:“Intel Corporation”,“doi-asserte-by”:“publisher”}],“content-domain”:{“domain”:[],“crossmark-restriction”:false},“short-container-title”:[“IEEE Trans.Multi-Scale Comp.Syst.”],“published-print”:{“date-parts”:[[2018,10,1]]},“DOI”:“10.1109\/tmscs.2018.2865303”,“type”:“journal-article”,“created”:{“date-parts”:[[2018,8,13]],“date-time”:“2018-08-13T18:40:43Z”,“timestamp”:1534185643000},”page“:”624-634“,”source“Crossref”,“is-referenced-by-count”:5,“title”:[“DeltaFrame-BP:一种使用帧差进行深度卷积神经网络训练和视频数据推断的算法”],“前缀”:“10.1109”,“卷”:“4”,“作者”:[{“ORCID”:“http://\/ORCID.org\/00000-0002-6526-4432”,“authenticated-ORCID”:false,“给定”:“Bing”,“family”:“Han”,”sequence“:“first”,“affiliation”:[]},{“给定的”:“Kaushik”,“家族”:“Roy”,“sequence”:“additional”,“affiliation”:[]}],“member”:“263”,“reference”:[{“key”:“ref39”,“article-title”:“deep neural networks under quantification”,“volume”:”abs 1511 6488“,“author”:“sung”,“year”:“2015”,“journal title”:,“author”:“courbariaux”,“year”:“2014”,“journal title”:“CoRR”},{“key”:“ref33”,“doi asserted by”:“publisher”,“doi”:“10.1109\/TED.2016.2568762”},{“key”:“ref32”,“doi asserted by”:“publisher”,“doi”:“10.1103\/PhysRevApplied.8064017”},{“key”:“ref31”,“首页”:“530”,“文章标题”:“磁隧道结激活的全自旋随机尖峰神经元网络”,“author”:“srinivasan”,“year”:“2017”,“journal-title”:“Proc-Des-Autom-Test-Eur-Conf-Exhib”},{“key”:“ref30”,“article-title“:”勘误:磁隧道结模拟随机皮层尖峰神经元“,”volume“:”7“,”author“:”sengupta“,”year“:”2017“},”{“密钥”:“ref37”,“article-title”:“少乘法的神经网络”,“volume”:“abs 1510 3009”,“author”:“lin”,“year”:“2015”,“journal-title“:”CoRR“},{“key”:”ref36“,”article-title“:”Deep learning with limited numerical precision“,”volume“:”abs 1502 2551“,”author“:”gupta“,”year“:”2015“,”journal-title,“内政部”:“10.1109\/ICCAD.2017.8203823”},{“key”:“ref34”,“article-title”:“全记忆电阻深脉冲神经网络:实现低功耗随机大脑的一步”,“author”:“wijesinghe”,“year”:“2017”,“journal-title“:“ArXiv e-prints”},“author”:“wen”,“year”:“2016”,“journal-title”:“Proc Advances Neural Inf Process Syst”},{“key”:“ref62”,“doi-asserted-by”:“publisher”,“doi”:“10.1145\/3131885.313906”}视频语义分割“,”卷“:“abs 1608 3609”,“author”:“shelhamer”,“year”:“2016”,“journal title”:“CoRR”},{“key”:“ref28”,“article title”:“基于STDP的尖峰神经网络中连接和权重量化的修剪,用于节能识别”,“volume”:“abs 1710 4734”,“author”:“rathi”,“year”:“2017”,“journal title”:“CoRR”},{“key”:“ref64”,“article title”:“用于大规模图像识别的极深卷积网络”,“卷”:“abs 1409 1556”,“作者”:“西蒙扬”,“年份”:“2014年”,“期刊标题”:“CoRR”},{“关键”:“参考27”,“文章标题”:《卷积棘突神经网络中基于时间的可塑性特征学习》,“卷“:“abs1703 3854”,“作家”:“熊猫”,“年”:“2017年”,“journal-title”:“CoRR”},{“key”:“ref65”,“article-title):“卷积神经网络的快速算法”,“volume”:”abs 1509 9308“,“author”:“lavin”,“year”:“2015”,“johnal-tittle”:《CoRR》},}“key:”ref66“,”doi-asserted-by“:”publisher“,”doi“10.1016\/0004-3702(81)90024-2“},”{“密钥”:“ref29”,“doi-assert-by”:“publisher”,“doi”:“10.1109\/TBCAS.2016.258223”},{“key”:“ref67”,“doi-asserted-by”:“publisher”,“doi”:“10.1145\/2733373.2807412”}ted-by“:”publisher“,”doi“:“10.1109\/CVPR.2016.213”},{“key”:“ref2”,“article-title”:“Microsoft COCO:上下文中的通用对象”,“volume”:”abs 1405 312“,”author“:”lin“,”year“:”2014“,”journal-title“:”CoRR“},”{“密钥”:“ref1”,“doi-asserted-by”:“publisher”,“doi”:“10.1007\/s11263-015-0816-y”}le-title“:”Maxout networks“,”author“:”goodfellow“,“年份”:“0”,“日志标题”:“Proc 30th Int Conf Mach Learn”},{“key”:“ref22”,“article-title”:“批量规范化:通过减少内部协变量偏移来加速深度网络训练”,“数量”:“abs 1502 3167”,“作者”:“ioffe”,“年”:“2015”,“日记标题”:”CoRR“},}“密钥”:“ref21”,“文章标题”:,“author”:“lin”,“year”:“2013”,“journal-title”:“CoRR”},{“key”:“ref24”,“first-pages”:“17”,“article-title“:”使用dropconnect正则化神经网络“,”author“:”wan“,”year“:”0,“volume”:“abs 1311 2524”,“author”:“girshick”,“year”:“2013”,“journal-title”:“CoRR”},{“key”:”ref26“,”doi-asserted-by“:”publisher“,”doi“:”10.1109\/JETCAS.2017.2769684“title”:“深度卷积神经网络的结构化剪枝”,“卷”:“abs 1512 8571”,“author”:“anwar”,“year”:“2015”,“journal-title”:“CoRR”},{“key”:“ref51”,“article-title(文章标题):“高效卷积的剪枝过滤器”,“volume”:”abs 1608 8710“,”author“:”li“,”year“:”2016“,”journal-title“:”CoRR“},”{“键”:“ref59”,“首页”:“806”,“文章标题”:“稀疏卷积神经网络”,“作者”:“liu”“,”年份“:”2015年“,“journal-title”:“Proc IEEE Conf Compute Vis Pattern Recognit”},{“key”:“ref58”,“article-title“:”Spatially-sparse卷积神经网络“,”volume“:”abs 1409 6070“,”author“:”graham“,”year“:”2014“,”journal-title“:”CoRR“},”{“密钥”:“ref57”,“doi-asserted-by”:“publisher”,“doi”:“10.1145\/300787.3001163”}“,{”key“:“ref56”,“物品标签”:“使用矢量量化压缩深度卷积网络”,“volume”:“abs 1412 6115”,“author”:“gong”,“year”:“2014”,“journal-title”:“CoRR”},{“key”:《ref55》,“article-title》:“Compressing neural networks with the hashing trick”,“volume”:“abs 1504 4788”,“author”:“chen”,“年份”:“2015”,“journal-ttitle”::“Squeezenet:Alexnet级精度,参数少50倍,模型大小<1mb”,“volume”:“abs 1602 7360”,“author”:“iandola”,“year”:“2016”,“journal-title”:“CoRR”},{“key”:”ref53“,“first page”:《81:1》,“article-title》:“近似人工神经网络的高效近似乘数设计”,“author”:“mrazek”,“year”:”2016“,“journal-title”:“Proc 35th Int Conf Compute-Aided Des”},{“key”:“ref52”,“first page”:”145“,“article-title“:”无乘法器人工神经元利用错误恢复能力进行节能神经计算“,”author“:”sarwar“,”year“:”2016“,”journal-title“:”Proc Des Autom Test Eur Conf Exhib“},”{“密钥”:“ref10”,“doi-asserted-by”:“publisher”,“doi”:“10.1109 \/MSP.2013.2241312”},{“key”:“ref11”,“doi-asserted-by”:“publisher”,“doi”:“10.109\/CVPR.2014.223”}doi-asserted-by“:”publisher“,”doi“:“10.1109\/TIP.2016.2531283”},{“key”:“ref13”,“article-title”:“Flownet:使用卷积网络学习光流”,“volume”:”abs 1504 6852“,”author“:”fischer“,”year“:”2015“,”journal-title“:”CoRR“},”{“密钥”:“ref14”,“文章-标题”:“学习从卷积神经网络视频中提取运动”,“volume”:“abs 1601 7532”,“author”:“teney”,“年份”:“2016”,“期刊标题”:“CoRR”},{“key”:“ref15”,“doi断言”:“publisher”,“doi”:“10.1145\/2474769.2744788”},{“key”:“ref16”,“文章标题”:“通过FFT快速训练卷积网络”,“volume”:“abs 1312 5851”,“author”:“mathieu”,“年份”:“2013”,“期刊标题”:“CoRR”},{“key”:“ref17”,“文章标题”:“带fbfft的快速卷积网络:GPU性能评估”,“卷”:“abs 1412 7580”,“作者”:“vasilache”,“年份”:“2014”,“日志标题”:“CoRR”},{“密钥”:“ref18”,“文章标题”:”cuDNN:深度学习的有效基元“,”卷“:”abs 1410 759“,”author“:”chetlur“,”年份“:”2014“,”日志标题“:”CoRR“},”{“key”:“ref19”,“article-ti”tle“:“深入卷积”,“卷”:“abs 1409 4842”,“作者”:“szegedy”,“年份”:“2014”,“新闻标题”:“CoRR”},{“key”:“ref4”,“doi-asserted-by”:“publisher”,“doi”:“10.1007\/s11263-010-0390-2”}“doi-asserted-by”:“publisher”,“doi”:“10.1162\/neco.2006.18.7.1527”},{“key”:“ref5”,“doi-asserted-by”:“publisher”,“doi”:“10.162\/089976602760128018”}7“,”首页“:“3371”,“article-title”:“堆叠去噪自动编码器:使用局部去噪标准学习深度网络中的有用表示”,“卷”:“11”,“作者”:“vincent”,“年份”:“2010”,“期刊标题”:“J Mach Learn Res”},{“key”:“ref49”,“article-titel”:“深度神经网络的无数据参数修剪”,“体积”:“abs 1507 6149”,”author“:“srinivas”,“year”:“2015”,“journal-title”:“CoRR”},{“key”:“ref9”,“doi-asserted-by”:“publisher”,“doi”:“10.1109”,TCSVT.2009.2014013“}”,{《key》:“ref46”,“article-title“:”使用期望反向传播训练二元多层神经网络进行图像分类“,”卷:“abs 1503 3562”,“author”:“cheng”,“年份”:“15”,“日记标题”:“CoRR”{,{“key”:“ref45”,“article-title”:“Bitwise neural networks”,“volume”:”abs 1601 6071“,“author”:“kim”,“year”:“2016”,“journal-title“:”CoRR“},{“key”:“ref48”,“article-title”:“Learning both weights and connections for effective neural networks”,”volume“:“abs 1506 2626”,“auder”:“han”,“年份”:“2015”,“journal-titel”:“CoRR”},“key“:”ref47“article”蒂尔”:“深度压缩:通过剪枝、训练量化和哈夫曼编码压缩深度神经网络”,“卷”:“abs 1510 149”,“作者”:“han”、“年份”:“2015”,“期刊标题”:“CoRR”},{“键”:“ref42”,“文章-标题”:”Binaryconnect:“在传播过程中用二进制权重训练深度神经网络,“年份”:“2015年”,“期刊标题”:“CoRR”},{“key”:“ref41”,“article-title”:“理解精确量化对神经网络准确性和能量的影响”,“volume”:《abs 1612 3940》,“author”:“hashemi”,“year”:“2016”,“journal-tittle”::“Binarynet:训练深度神经网络,权重和激活限制为+1或-1”,“volume”:“abs 1602 2830”,“author”:“courbariaux”,“year”:“2016”,“journal-title”:“CoRR”},{“key”:”ref43“,“article-title“:”Xnor-net:使用二进制卷积神经网络进行Imagenet分类“,”volume“:”abs 1603 5279“,”author“:”rastegari“,”year“:“2016”,“journal-title”:“CoRR”}],“container-title“:[”IEEE Transactions on Multi-Scale Computing Systems“],“original-title:[],“link”:[{“URL”:“https:\/\/ieeexplore.IEEE.org\/ielaam\/668731502\/8434311-aam.pdf”,“content-type”:“application\/pdf”,“content-version”:“am”,“intended-application”:“syndication”},{“URL”:“http://explorestaging.ieee.org/ielx7\/6687315\/8630102\/0434331.pdf?arnumber=834331”,“内容类型”:“未指定”,“内容版本”:“vor”,“预期应用程序”:“相似性检查”}],“存放”:{“日期部分”:[[2022,4,8]],“日期时间”:“2022-04-08T18:54:26Z”,“时间戳”:1649444066000},“分数”:1,“资源”:{“主要”:{“URL”:“https:\/\/ieeexplore.iee.org\/document\/8434331\/”}},“副标题”:[],“短标题”:[],“已发布”:{“日期-部件”:[[2018,10,1]]},”引用计数“:69,”日志发布“:{”问题“:“4”}、“URL”:“http://\/dx.doi.org\/10.109\/tmscs.2018.2865303”,“关系”:{},“2332-7766”,“2372-207X”],“ISSN-type”:[{“value”:“23327766”,“type”:“electronic”},{“value”:“2372-207X”,“type”:“electronic”}],“subject”:[],“published”:{“date-parts”:[[2018,10,1]]}}