{"title":"1.1 Deep Learning Hardware: Past, Present, and Future","authors":"Yann LeCun","doi":"10.1109/ISSCC.2019.8662396","DOIUrl":null,"url":null,"abstract":"Historically, progress in neural networks and deep learning research has been greatly influenced by the available hardware and software tools. This paper identifies trends in deep learning research that will influence hardware architectures and software platforms of the future.","PeriodicalId":265551,"journal":{"name":"2019 IEEE International Solid- State Circuits Conference - (ISSCC)","volume":"67 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"89","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 IEEE International Solid- State Circuits Conference - (ISSCC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISSCC.2019.8662396","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 89
Abstract
Historically, progress in neural networks and deep learning research has been greatly influenced by the available hardware and software tools. This paper identifies trends in deep learning research that will influence hardware architectures and software platforms of the future.
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