Loading…
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...
Saved in:
Published in: | Computer (Long Beach, Calif.) Calif.), 2022-07, Vol.55 (7), p.18-28 |
---|---|
Main Authors: | , , , , , , , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
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. |
---|---|
ISSN: | 0018-9162 1558-0814 |
DOI: | 10.1109/MC.2022.3148714 |