Achieving Green AI with Energy-Efficient Deep Learning Using Neuromorphic Computing

Abstract

research-article Free Access Share on Achieving Green AI with Energy-Efficient Deep Learning Using Neuromorphic Computing Authors: Tao Luo Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (ASTAR), in Singapore Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (ASTAR), in SingaporeSearch about this author , Weng-Fai Wong National University of Singapore (NUS), Singapore National University of Singapore (NUS), SingaporeSearch about this author , Rick Siow Mong Goh Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (ASTAR), in Singapore Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (ASTAR), in SingaporeSearch about this author , Anh Tuan Do Institute of Microelectronics (IME), Agency for Science, Technology and Research (ASTAR), in Singapore Institute of Microelectronics (IME), Agency for Science, Technology and Research (ASTAR), in SingaporeSearch about this author , Zhixian Chen Institute of Microelectronics (IME), Agency for Science, Technology and Research (ASTAR), in Singapore Institute of Microelectronics (IME), Agency for Science, Technology and Research (ASTAR), in SingaporeSearch about this author , Haizhou Li National University of Singapore (NUS), Singapore National University of Singapore (NUS), SingaporeSearch about this author , Wenyu Jiang Institute for Infocomm Research (I2R), Agency for Science, Technology and Research (ASTAR), in Singapore Institute for Infocomm Research (I2R), Agency for Science, Technology and Research (ASTAR), in SingaporeSearch about this author , Weiyun Yau Institute for Infocomm Research (I2R), Agency for Science, Technology and Research (ASTAR), in Singapore Institute for Infocomm Research (I2R), Agency for Science, Technology and Research (ASTAR), in SingaporeSearch about this author Authors Info & Claims Communications of the ACMVolume 66Issue 7July 2023pp 52–57https://doi.org/10.1145/3588591Published:22 June 2023Publication History 0citation0DownloadsMetricsTotal Citations0Total Downloads0Last 12 Months0Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteView all FormatsPDF