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

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Achieving Green AI with Energy-Efficient Deep Learning Using Neuromorphic Computing
Title:
Achieving Green AI with Energy-Efficient Deep Learning Using Neuromorphic Computing
Journal Title:
Communications of the ACM
Publication Date:
22 June 2023
Citation:
Luo, T., Wong, W.-F., Goh, R. S. M., Do, A. T., Chen, Z., Li, H., Jiang, W., & Yau, W. (2023). Achieving Green AI with Energy-Efficient Deep Learning Using Neuromorphic Computing. Communications of the ACM, 66(7), 52–57. https://doi.org/10.1145/3588591
Abstract:
DEEP LEARNING (DL) systems have been widely adopted in many industrial and business applications, dramatically improving human productivity, and enabling new industries. However, deep learning has a carbon emission problem. For example, training a single DL model can consume as much as 656,347 kilowatt-hours of energy and generate up to 626,155 pounds of CO2 emissions, approximately equal to the total lifetime carbon footprint of five cars. Therefore, in pursuit of sustainability, the computational and carbon costs of DL have to be reduced.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the National Research Foundation - RIE2020 - Advanced Manufacturing and Engineering
Grant Reference no. : A1687b0033
Description:
© Author | ACM 2023. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in Communications of the ACM, http://dx.doi.org/10.1145/10.1145/3588591
ISSN:
0001-0782
1557-7317
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