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
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Authors
10Topics & keywords
Keywords
- Carbon footprint
- Computer science
- Footprint
- Plateau (mathematics)
- Training (meteorology)
- Raising (metalworking)
- Memory footprint
- Carbon fibers
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