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

Reference Type:

Journal Article

Patterson, David, Joseph Gonzalez, Urs Hölzle, Quoc Le, Chen Liang, Lluis-Miquel Munguia, Daniel Rothchild, David R. So, Maud Texier, and Jeff Dean. 2022. “The Carbon Footprint of Machine Learning Training Will Plateau, Then Shrink.” Computer 55 (7): 18–28. https://doi.org/10.1109/MC.2022.3148714

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.