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Sustainable AI: Environmental Implications, Challenges and Opportunities

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arxiv 2111.00364 v2 pith:USXFPSZO submitted 2021-10-30 cs.LG cs.AIcs.AR

Carole-Jean Wu , Ramya Raghavendra , Udit Gupta , Bilge Acun , Newsha Ardalani , Kiwan Maeng , Gloria Chang , Fiona Aga Behram
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James Huang Charles Bai Michael Gschwind Anurag Gupta Myle Ott Anastasia Melnikov Salvatore Candido David Brooks Geeta Chauhan Benjamin Lee Hsien-Hsin S. Lee Bugra Akyildiz Maximilian Balandat Joe Spisak Ravi Jain Mike Rabbat Kim Hazelwood
This is my paper · ORCID
classification cs.LGcs.AIcs.AR
keywords carbonfootprintacrosschallengescomputingcycledevelopmentenvironmental
verification ladder T0 review T1 audit T2 compute T3 formal
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This paper explores the environmental impact of the super-linear growth trends for AI from a holistic perspective, spanning Data, Algorithms, and System Hardware. We characterize the carbon footprint of AI computing by examining the model development cycle across industry-scale machine learning use cases and, at the same time, considering the life cycle of system hardware. Taking a step further, we capture the operational and manufacturing carbon footprint of AI computing and present an end-to-end analysis for what and how hardware-software design and at-scale optimization can help reduce the overall carbon footprint of AI. Based on the industry experience and lessons learned, we share the key challenges and chart out important development directions across the many dimensions of AI. We hope the key messages and insights presented in this paper can inspire the community to advance the field of AI in an environmentally-responsible manner.

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Cited by 4 Pith papers

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