Carbon Emissions Reduction of Neural Network by Discrete Rank Pruning

Author:

Pei SongwenORCID,Luo Jie,Liang Sheng,Ding Haonan,Ye Xiaochun,Chen Mingsong

Funder

National Natural Science Foundation of China

Natural Science Foundation of Shanghai

State Key Laboratory of Computer Architecture

Key Laboratory of Engineering Dielectrics and Its Application (Harbin University of Science and Technology), Ministry of Education

Publisher

Springer Science and Business Media LLC

Subject

Information Systems,Hardware and Architecture,Computer Science Applications,Computer Science (miscellaneous)

Reference30 articles.

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3. Hassibi, B., Stork, D.: Second order derivatives for network pruning: optimal brain surgeon. Adv. Neural Inf. Process. Syst. 5 (1992)

4. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)

5. Henderson, P., Hu, J., Romoff, J., Brunskill, E., Jurafsky, D., Pineau, J.: Towards the systematic reporting of the energy and carbon footprints of machine learning. J. Mach. Learn. Res. 21(1), 10039–10081 (2020)

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