{"title":"Towards Energy-Efficient Computing Hardware Based on Memristive Nanodevices","authors":"Y. Huang, Vignesh Ravichandran, Wuyu Zhao, Q. Xia","doi":"10.1109/MNANO.2023.3297106","DOIUrl":null,"url":null,"abstract":"Computing hardware is one of the crucial drivers of artificial intelligence (AI) that impacts our daily lives. However, despite the significant improvements made in recent decades, the energy consumption of computing hardware that powers AI, especially deep neural networks, remains considerably higher than that of human brains. Hardware innovations based on emerging nanodevices like memristors offer potential solutions to energy-efficient computing systems. This review discusses the challenges associated with developing energy-efficient computing hardware based on memristive nanodevices and summarizes recent progress in memristive devices, crossbar arrays, systems, and algorithms, aiming at addressing these issues from a bottom-up approach. Potential research directions are proposed to further improve future computing hardware's energy efficiency.","PeriodicalId":44724,"journal":{"name":"IEEE Nanotechnology Magazine","volume":"17 1","pages":"30-38"},"PeriodicalIF":2.3000,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Nanotechnology Magazine","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MNANO.2023.3297106","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"NANOSCIENCE & NANOTECHNOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Computing hardware is one of the crucial drivers of artificial intelligence (AI) that impacts our daily lives. However, despite the significant improvements made in recent decades, the energy consumption of computing hardware that powers AI, especially deep neural networks, remains considerably higher than that of human brains. Hardware innovations based on emerging nanodevices like memristors offer potential solutions to energy-efficient computing systems. This review discusses the challenges associated with developing energy-efficient computing hardware based on memristive nanodevices and summarizes recent progress in memristive devices, crossbar arrays, systems, and algorithms, aiming at addressing these issues from a bottom-up approach. Potential research directions are proposed to further improve future computing hardware's energy efficiency.
期刊介绍：
IEEE Nanotechnology Magazine publishes peer-reviewed articles that present emerging trends and practices in industrial electronics product research and development, key insights, and tutorial surveys in the field of interest to the member societies of the IEEE Nanotechnology Council. IEEE Nanotechnology Magazine will be limited to the scope of the Nanotechnology Council, which supports the theory, design, and development of nanotechnology and its scientific, engineering, and industrial applications.
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