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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BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
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Xception: Deep Learning with Depthwise Separable Convolutions
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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
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Self-citationLinguistically-Informed Self-Attention for Semantic Role Labeling
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Self-citationQuantifying the Carbon Emissions of Machine Learning
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Massively Multilingual Neural Machine Translation
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Cited by
Sorted by citations
High-Resolution Image Synthesis with Latent Diffusion Models
Rombach, R., Blattmann, A., Lorenz, D., Esser, P., & Ommer, B. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01042 · Cited by 20,302
Physics for neuromorphic computing
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Challenges in deploying machine learning: A survey of case studies
Paleyes, A., Urma, R.-G., & Lawrence, N. D. (2023) · ACM Computing Surveys · doi:10.1145/3533378 · Cited by 638
Edge learning using a fully integrated neuro-inspired memristor chip
Zhang, W., Yao, P., Gao, B., Liu, Q., Wu, D. M., Zhang, Q., Li, Y., Qin, Q., Li, J., Zhu, Z., et al. (2023) · Science · doi:10.1126/science.ade3483 · Cited by 454
Fan, Z., Yan, Z., & Wen, S. (2023) · Sustainability · doi:10.3390/su151813493 · Cited by 417
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
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