Cited by 774Green IT and SustainabilityEnergy Efficiency in ComputingEthics and Social Impacts of AI

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Energy and Policy Considerations for Modern Deep Learning Research

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Strubell, Emma, Ganesh, Ananya, McCallum, Andrew·2020·Proceedings of the AAAI Conference on Artificial Intelligence

DOI 10.1609/aaai.v34i09.7123

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Abstract

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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Authors: Strubell, E., Ganesh, A., & McCallum, A.
Affiliations: Meta (United States) (US)
University of Massachusetts Amherst (US)
ORCID: Strubell, Emma
Ganesh, Ananya
McCallum, Andrew
Venue: Proceedings of the AAAI Conference on Artificial Intelligence
Publisher: Association for the Advancement of Artificial Intelligence (AAAI)
Published: 2020-04-03
Volume: 34
Issue: 09
Pages: 13693-13696
ISSN: 2159-5399 (ISSN-L)
2374-3468
DOI: 10.1609/aaai.v34i09.7123
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

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Energy and Policy Considerations for Modern Deep Learning Research

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