{“状态”:“确定”,“消息类型”:“工作”,“信息版本”:“1.0.0”,“邮件”:{“索引”:{“日期部分”:[[2024,6,1]],“日期时间”:“2024-06-01T11:07:25Z”,“时间戳”:1717240045092},“参考计数”:14,“出版商”:“电气与电子工程师学会(IEEE)”,“问题”:“1”,“许可证”:[{“开始”:{'日期部分“:[2021,1,1]],”日期时间我”:“2021-01-01T00:00:00Z”,“timestamp”:1609459200000},“content-version”:“vor”,“delay-in-days”:0,“URL”:“https:\/\/ieeexplore.iee.org\/Xplorehelp\/downloads\/license-information\/ieee.html”},{“start”:{“date-parts”:[2021,1,1]],“date-time”:“2021-01T00:00Z”,”timestamp“:160945.9200000},“content-version”:“stm-asf“,”delay-in-days“:0,”URL“:“https:\/\/doi.org\/10.15223\/policy-029”},{“start”:{“date-parts”:[[2021,1,1]],“date-time”:“2021-01-01T00:00:00Z”,“timestamp”:1609459200000},“content-version”:“stm-asf”,“delay-in-days”:0,“URL”:“http:\/\/doi.org\/10.15223\/policy-037”}],“content-domain”:{“domain”:[],“crossmark-restrict”ion“:false},”short-container-title“:[”Computer“],“published-print”:{“date-parts”:[[2021,1]]},“DOI”:“10.1109\/mc.2020.3034951”,“type”:“journal-article”,“created”:{“date-parts”:[2021,1,14]],“date-time”:“2021-01-14T20:34:05Z”,“timestamp”:1610656445000},”page“:”84-88“source”:”Crossref“,”is-referenced-by-count“:10,”title“:[”Edge Artificial Intelligence Chips for the Cyber物理系统时代“],“前缀”:“10.1109”,“卷”:“54”,“作者”:[{“给定”:“广岛”,“家族”:“福克塔”,“序列”:“第一”,“从属关系”:[}“名称”:“日本东京国家先进工业科学技术研究院人工智能芯片设计开放创新实验室”}]},{“给出”:“Kunio”,“家庭”:“Uchiyama”,“顺序”:“附加”,“附属关系”::“日本东京国家先进工业科学技术研究院人工智能芯片设计开放创新实验室”}]}],“成员”:“263”,“参考”:[{“key”:“ref1”,“volume-title”:“AI and compute”,“year”:“2020”},{“key”:”ref2“,”doi-asserted-by“:”publisher“,”doi“:”10.1109\/JPROC.2017.2761740“},“key“:”ref3“,”首页“:”7675“,”article-title“:“用8位浮点数字训练深度神经网络”,“卷标题”:“Proc.NeurIPS”,“作者”:“王”,“年份”:“2018”},{“键”:“ref4”,“doi断言者”:“publisher”,“doi”:“10.1109\/ISCAS.2018.8350953”},{“键”:“ref5”,“doi断言者”:“publisher”,“doi”:“10.1109\/HOTCHIPS.206.7936208”},{“键”:“ref6”,“卷标题”:“深度学习的有效方法和硬件”,“author”:“Han”,“year”:“2017”},{“key”:“ref7”,“作者”:“Courbariaux”,“年份”:“2016”,“journal-title”:“二值化神经网络:训练权重和激活限制为1或1的神经网络”},{“key”:“ref9”,“author”:“Rastegari”,“year”:“2016”,“journal-title”:“XNOR-Net:ImageNet classification using binary convolutional neural networks”},{“key”:“ref10”,“doi-asserted-by”:“publisher”,“doi”:“10.1587\/transinf.2018RCP0002”}“:”ref13“,”volume-title“:“Edge TPU”,“year”:“2018”},{“key”:“ref14”,“doi-asserted-by”:“publisher”,“doi”:“10.1109\/ISSCC19947.2020.9063111”}explorestaging.ieee.org\/ielx7\/2\/9321788\/09321799.pdf?arnumber=9321799“,“content-type”:“unspecified”,“content-version”:“vor”,“intended-application”:“similarity-checking”}],“deposed”:{“date-parts”:[[2024,1,24]],“date-time”:”2024-01-24T01:18:48Z“,“timestamp”:170605912800},“score”:1,“resource”:{primary“:{”URL“https:\//ieeexplore.iee.org\/document\/9321799\/“}}”,“subtitle”:[],“短标题”:[],“已发布”:{“日期-部分”:[2021,1]]},“引用-计数”:14,“日志-问题”:{“问题”:“1”},”URL“:”http://\/dx.doi.org\/10.109\/mc.2020.3034951“,”关系“:{},‘ISSN’:[”0018-9162“,”1558-0814“],“ISSN-type”:[{“value”:“0018-9162',”type“:”print“},{“值”:“1558-0814',”类型“:”electronic“}],“主题”:[],“已发布”:{“日期部分”:[2021,1]]}}}