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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Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018) · arXiv · doi:10.48550/arxiv.1810.04805 · Cited by 73,173
Xception: Deep Learning with Depthwise Separable Convolutions
Chollet, F. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.195 · Cited by 20,380
Deep Contextualized Word Representations
Peters, M., Neumann, M., Iyyer, M., Gardner, M., Clark, C., Lee, K., & Zettlemoyer, L. (2018) · Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) · doi:10.18653/v1/n18-1202 · Cited by 11,983
Mastering the game of Go without human knowledge
Silver, D., Schrittwieser, J., Simonyan, K., Antonoglou, I., Huang, A., Guez, A., Hubert, T., Baker, L., Lai, M., Bolton, A., et al. (2017) · Nature · doi:10.1038/nature24270 · Cited by 9,084
Large Scale GAN Training for High Fidelity Natural Image Synthesis
Brock, A., Donahue, J., & Simonyan, K. (2018) · arXiv · doi:10.48550/arxiv.1809.11096 · Cited by 3,717
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
Lacoste, A., Luccioni, A., Schmidt, V., & Dandres, T. (2019) · arXiv · doi:10.48550/arxiv.1910.09700 · Cited by 408
Massively Multilingual Neural Machine Translation
Aharoni, R., Johnson, M., & Firat, O. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1388 · Cited by 402
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Penteado, B. E., Fornazin, M., Castro, L., Rachid, R., Gonçalves, L. H. D. N., & Falcão, M. Z. (2025) · Health and Technology · doi:10.1007/s12553-025-01015-0 · Cited by 4
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Tirel, N., Roose, P., Ilarri, S., Noureddine, A., & Goaër, O. L. (2025) · ACM Computing Surveys · doi:10.1145/3769301 · Cited by 2
Schuck, A. D. F., Garcia, G. L., Manesco, J. R. R., Paiola, P. H., & Papa, J. P. (2025) · Journal of the Brazilian Computer Society · doi:10.5753/jbcs.2025.5793 · Cited by 4
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Ghawaly, J., Nicholson, A., Schuman, C. D., Diez, D., Young, A. R., & Witherspoon, B. (2025) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn64981.2025.11228041 · Cited by 2
Position Paper: The Environmental Cost of Deep Learning: A Call for Sustainable AI Practices
Fourati, R. (2025) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn64981.2025.11228533 · Cited by 3
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Drage, E., Pi, Y., Hollanek, T., Peters, D., & Yakar, S. (2025) · AI and Ethics · doi:10.1007/s43681-025-00885-4 · Cited by 5
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Li, Y., Cai, Y., Qi, X., Liu, S., Zhuang, X., Zheng, H., Tian, Y., Zhu, Y. P., Cao, W., & Zhang, X. (2025) · Plant Phenomics · doi:10.1016/j.plaphe.2025.100144 · Cited by 3
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Sihare, S. R. (2025) · Quantum Machine Intelligence · doi:10.1007/s42484-025-00324-x · Cited by 3
Carbon‐14 Perovskite Betavoltaics Reach Record 10.79% Efficiency
Kim, H. S., Rhee, R. J. K., Lee, J., Naseem, M. B., Lee, J. H., Park, J. H., & In, S. (2025) · Carbon Energy · doi:10.1002/cey2.70149 · Cited by 2
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Gaurav, R., Agarwal, S., Stewart, T. C., & Yi, Y. (2025) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2025.3605627 · Cited by 2
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Kang, Y., & Wang, C. (2026) · International Journal of Cartography · doi:10.1080/23729333.2025.2582231 · Cited by 2
Topologically Protected Graphene Metamaterials for Terahertz Biosensing: A Comprehensive Review
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Wang, Y., Zhong, M., Liao, K., Sun, Q., Hu, X., & Gong, Q. (2026) · Nanophotonics · doi:10.1002/nap2.70098 · Cited by 2
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Su, M., Wang, S., & Xiao, Y. (2026) · International Review of Economics & Finance · doi:10.1016/j.iref.2026.105659 · Cited by 2
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Petrov, M. O., Ryndin, E. A., & Andreeva, N. V. (2024) · The European Physical Journal Special Topics · doi:10.1140/epjs/s11734-024-01369-y · Cited by 2
Martínez‐Fernández, S. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-78386-9_1 · Cited by 2
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Singh, D., & Terayama, A. (2024) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata62323.2024.10825282 · Cited by 2
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Kim, E. M., Carpenter, L. G., Smith, A. M., Tison, C. C., Coleman, D. J., Leake, G., Fanto, M. L., & Rizzo, A. J. (2025) · Integrated Optics: Devices, Materials, and Technologies · doi:10.1117/12.3043377 · Cited by 2
Gayap, H. T., & Akhloufi, M. A. (2025) · Applied Biosciences · doi:10.3390/applbiosci4010011 · Cited by 2
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Wu, W., & Ma, D. (2025) · Environmental Technology & Innovation · doi:10.1016/j.eti.2025.104273 · Cited by 2
Wang, C., & Liu, H. (2025) · Symmetry · doi:10.3390/sym17060891 · Cited by 2
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Tsoury, A., & Barnett‐Itzhaki, Z. (2025) · Sustainability · doi:10.3390/su17146336 · Cited by 2
Rahman, M. H., Haider, Z., Rizvee, M. M., Shomaji, S., & Chakraborty, P. (2025) · IEEE Access · doi:10.1109/access.2025.3592039 · Cited by 2
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Nepote, L., Lhéritier, A., Bondoux, N., Kountouris, M., & Filippone, M. (2025) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn64981.2025.11228637 · Cited by 2
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Morik, K. J., Kotthaus, H., Heppe, L., Heinrich, D., Fischer, R., Pauly, A., & Piatkowski, N. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.00512 · Cited by 4
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Vergallo, R., Cruz, L., Errico, A., & Mainetti, L. (2024) · Research Square · doi:10.21203/rs.3.rs-4747830/v1 · Cited by 2
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Bell, J., Quarantiello, L., Coleman, E. N., Li, L., Li, M., Madeddu, M., Piccoli, E., & Lomonaco, V. (2025) · arXiv · doi:10.48550/arxiv.2506.03320 · Cited by 2
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Patil, M. (2024) · Journal of Electrical Systems · doi:10.52783/jes.3221 · Cited by 1
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Morehouse, H. (2025) · Memory Mind & Media · doi:10.1017/mem.2025.10017 · Cited by 1
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Rabbi, M. F. (2026) · Discover Artificial Intelligence · doi:10.1007/s44163-026-01045-1 · Cited by 1
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Lee, M., Han, J. H., Yang, C., Choi, K., & Nam, S. (2026) · Applied Surface Science · doi:10.1016/j.apsusc.2026.166674 · Cited by 1
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Rahman, S., Mallick, I., Malaker, T., Mohsin, A. K. M., & Ahmed, M. E. (2026) · Sustainable Development · doi:10.1002/sd.71036 · Cited by 1
Dar, A. A., Arora, V., & Yas, H. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-7554-0.ch006 · Cited by 1
Chajdas, T. (2026) · Springer eBooks · doi:10.1007/978-3-032-16800-9_5 · Cited by 1
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Dauve, S., & Cioccio, L. D. (2023) · Annales des Mines - Responsabilité et environnement · doi:10.3917/re1.110.0095 · Cited by 1
Rotary: A Resource Arbitration Framework for Progressive Iterative Analytics
Liu, R., Elmore, A. J., Franklin, M. J. M., & Krishnan, S. (2023) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde55515.2023.00166 · Cited by 1
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Spratling, M. (2025) · Neural Networks · doi:10.1016/j.neunet.2025.107801 · Cited by 5
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Bourg, C., Kriegsman, S., Lindsay, N., Sardis, H., Stalberg, E., & Altman, M. (2024) · doi:10.21428/e4baedd9.567bfd15 · Cited by 1
Mishra, D. P., Agarwal, N., Shah, D., & Salkuti, S. R. (2024) · International Journal of Informatics and Communication Technology (IJ-ICT) · doi:10.11591/ijict.v13i1.pp116-122 · Cited by 2
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Kumar, A., Park, J., Zhou, Y., Kim, J.-H., Jain, S., Schuman, C. D., Cauwenberghs, G., & Kuzum, D. (2024) · Neuro Inspired Computational Elements Conference (NICE) · doi:10.1109/nice61972.2024.10549369 · Cited by 1
Foglia, K. R., Colao, V., & Ritacco, E. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-62700-2_26 · Cited by 1
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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