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