The Carbon Footprint of Machine Learning Training Will Plateau, Then Shrink

Machine learning (ML) workloads have rapidly grown, raising concerns about their carbon footprint. We show four best practices to reduce ML training energy and carbon dioxide emissions. If the whole ML field adopts best practices, we predict that by 2030, total carbon emissions from training will de...

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Bibliographic Details
Published in:Computer (Long Beach, Calif.) Vol. 55; no. 7; pp. 18 - 28
Main Authors: Patterson, David, Gonzalez, Joseph, Holzle, Urs, Le, Quoc, Liang, Chen, Munguia, Lluis-Miquel, Rothchild, Daniel, So, David R., Texier, Maud, Dean, Jeff
Format: Journal Article
Language:English
Published: New York IEEE 01.07.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Best practice
Best practices
Carbon dioxide
Carbon footprint
Emissions
Footprint analysis
Machine learning
Training
Training data
ISSN:0018-9162, 1558-0814
Online Access:Get full text
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Summary:Machine learning (ML) workloads have rapidly grown, raising concerns about their carbon footprint. We show four best practices to reduce ML training energy and carbon dioxide emissions. If the whole ML field adopts best practices, we predict that by 2030, total carbon emissions from training will decline.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 14
ISSN:0018-9162
1558-0814
DOI:10.1109/MC.2022.3148714