articleComputerJun 28, 2022Closed access

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

University of California, Berkeley · Google (United States)

Indexed incrossref

Abstract

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.

Citation impact

288
total citations
FWCI
34.82
Percentile
100%
References
23
Citations per year

Authors

10

Topics & keywords

Keywords
  • Carbon footprint
  • Computer science
  • Footprint
  • Plateau (mathematics)
  • Training (meteorology)
  • Raising (metalworking)
  • Memory footprint
  • Carbon fibers
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