Green AI: do deep learning frameworks have different costs?
Abstract
The use of Artificial Intelligence (ai), and more specifically of Deep Learning (dl), in modern software systems, is nowadays widespread and continues to grow. At the same time, its usage is energy demanding and contributes to the increased CO2 emissions, and has a great financial cost as well. Even though there are many studies that examine the capabilities of dl, only a few focus on its green aspects, such as energy consumption.
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Studies illustrate significant disparities in energy consumption and economic costs during training and inference between various deep learning frameworks.
“Moreover, studies illustrate significant disparities in energy consumption and economic costs during training and inference between various frameworks (Georgiou et al., 2022).”
From CodeTransOcean: A Comprehensive Multilingual Benchmark for Code Translation (2023) · cites this paper as Georgiou et al., 2022
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474 · see citing works
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155 · see citing works
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
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Statistical Power Analysis for the Behavioral Sciences
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A systematic review of Green <scp>AI</scp>
Verdecchia, R., Sallou, J., & Cruz, L. (2023) · WIREs Data Mining and Knowledge Discovery · doi:10.1002/widm.1507 · Cited by 375 · see citing works
CodeTransOcean: A Comprehensive Multilingual Benchmark for Code Translation
Yan, W., Tian, Y., Li, Y., Chen, Q., & Wang, W. (2023) · Findings of the Association for Computational Linguistics: EMNLP 2023 · doi:10.18653/v1/2023.findings-emnlp.337 · Cited by 31 · see citing works
- Authors
- Georgiou, S., Kechagia, M., Sharma, T., Sarro, F., & Zou, Y.
- Affiliations
- Queen's University (CA)
- University College London (GB)
- Dalhousie University (CA)
- Venue
- Proceedings of the 44th International Conference on Software Engineering
- Publisher
- ACM
- Published
- 2022-05-21
- Pages
- 1082-1094
- ISSN
- 0270-5257 (ISSN-L)
- OpenAlex
- W4284664530
- Cited by
- 102
- References
- 6
- Citations by year
- 2026: 21 · 2025: 33 · 2024: 22 · 2023: 22 · 2022: 4
- FWCI
- 32.76
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) (publishedVersion)
- Funders
- Epic Foundation (741278)
- 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 › Artificial Intelligence Applications
- Keywords
- green AI, deep learning, energy consumption, CO2 emissions, environmental impact of AI, energy efficiency, software systems
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