The DOI matches its Crossref record, with a small difference in title or year; 14 of the 14 references it cites were found in a registry or an index.
Energy and Policy Considerations for Modern Deep Learning Research
Open accessAbstract
The field of artificial intelligence has experienced a dramatic methodological shift towards large neural networks trained on plentiful data. This shift has been fueled by recent advances in hardware and techniques enabling remarkable levels of computation, resulting in impressive advances in AI across many applications. However, the massive computation required to obtain these exciting results is costly both financially, due to the price of specialized hardware and electricity or cloud compute time, and to the environment, as a result of non-renewable energy used to fuel modern tensor processing hardware. In a paper published this year at ACL, we brought this issue to the attention of NLP researchers by quantifying the approximate financial and environmental costs of training and tuning neural network models for NLP (Strubell, Ganesh, and McCallum 2019). In this extended abstract, we briefly summarize our findings in NLP, incorporating updated estimates and broader information from recent related publications, and provide actionable recommendations to reduce costs and improve equity in the machine learning and artificial intelligence community.
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Mastering the game of Go without human knowledge
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Large Scale GAN Training for High Fidelity Natural Image Synthesis
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Energy and Policy Considerations for Deep Learning in NLP
Strubell, E., Ganesh, A., & McCallum, A. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1355 · Cited by 3,684
Self-citationLinguistically-Informed Self-Attention for Semantic Role Labeling
Strubell, E., Verga, P. W., Andor, D., Weiss, D., & McCallum, A. (2018) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d18-1548 · Cited by 426
Self-citationQuantifying the Carbon Emissions of Machine Learning
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Massively Multilingual Neural Machine Translation
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Jayakrishnan, A. R., Hellenbrand, M., Dixon, S. C., Mehonić, A., Silva, J., & MacManus‐Driscoll, J. L. (2025) · APL Machine Learning · doi:10.1063/5.0270385 · Cited by 2
Metadata
- Authors
- Strubell, E., Ganesh, A., & McCallum, A.
- Affiliations
- Meta (United States) (US)
- University of Massachusetts Amherst (US)
- Venue
- Proceedings of the AAAI Conference on Artificial Intelligence
- Published
- 2020-04-03
- Volume
- 34
- Issue
- 09
- Pages
- 13693-13696
- ISSN
- 2159-5399 (ISSN-L)
- 2374-3468
- OpenAlex
- W3037032032
- Cited by
- 774
- References
- 14
- Citations by year
- 2026: 143 · 2025: 307 · 2024: 129 · 2023: 96 · 2022: 68 · 2021: 30 · 2020: 1
- FWCI
- 68.55
- Type
- conference paper
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- www.aaai.org/ (unspecified)
- Locations
- Proceedings of the AAAI Conference on Artificial Intelligence (publishedVersion)
- AAAI Publications (The Association for the Advancement of Artificial Intelligence (AAAI)) (submittedVersion)
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Green IT and Sustainability
- Physical Sciences › Computer Science › Hardware and Architecture › Energy Efficiency in Computing
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Keywords
- carbon footprint of AI, large-scale neural networks, tensor processing units, environmental impact of machine learning, energy policy, natural language processing
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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