Green AI for Image Processing Systems: Toward Energy-Efficient and Environmentally Sustainable Computer Vision
Keywords:
Convolutional neural networks, Energy efficiency, Green AI, Image processing, Model compression, Sustainable computingAbstract
Deep learning has become the dominant paradigm for image processing tasks such as classification, detection, segmentation, and generation, but the accuracy gains of the last decade have been achieved largely through ever larger models, higher-resolution inputs, and longer training schedules. This trajectory carries a growing energy and carbon cost that is increasingly at odds with sustainability goals and with the practical constraints of mobile, embedded, and edge deployment. Green AI reframes efficiency, alongside accuracy, as a first-class evaluation criterion for machine learning research and practice. This article examines the role of Green AI principles specifically within image processing systems. It reviews the sources of computational and environmental cost in modern computer vision pipelines, surveys architectural and algorithmic strategies for reducing that cost, including lightweight convolutional architectures, pruning, quantization, and knowledge distillation, and discusses tools for measuring and reporting energy and carbon footprints. Comparative tables summarize representative lightweight architectures, compression techniques, and measurement frameworks, while accompanying figures illustrate the Green AI lifecycle, a representative compression pipeline, and empirical efficiency-accuracy trade-offs. The article concludes by outlining open challenges and future directions for building image processing systems that are simultaneously accurate, efficient, and environmentally responsible.
References
R. Schwartz, J. Dodge, N. A. Smith, and O. Etzioni, “Green AI,” Communications of the ACM, vol. 63, no. 12, pp. 54–63, Nov. 2020.
E. Strubell, A. Ganesh, and A. McCallum, “Energy and policy considerations for deep learning in NLP,” in Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, 2019, pp. 3645–3650.
D. Patterson et al., Carbon emissions and large neural network training, arXiv, Apr. 2021.
A. Lacoste, A. Luccioni, V. Schmidt, and T. Dandres, “Quantifying the carbon emissions of machine learning,” arXiv, 2019.
C.-J. Wu et al., “Sustainable AI: Environmental implications, challenges and opportunities,” arXiv, Oct. 2021.
R. Verdecchia, J. Sallou, and L. J. Cruz, “A systematic review of Green AI,” Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery, vol. 13, no. 4, Jun. 2023.
J. Xu, W. Zhou, Z. Fu, H. Zhou, and L. Li., “A survey on green deep learning,” arXiv, Nov. 2021.
P. Henderson, J. Hu, J. Romoff, E. Brunskill, D. Jurafsky, and J. Pineau, “Towards the systematic reporting of the energy and carbon footprints of machine learning,” Journal of Machine Learning Research, vol. 21, pp.1-43, Nov. 2020.
L. Lannelongue, J. Grealey, and M. Inouye, “Green algorithms: Quantifying the carbon footprint of computation,” Advanced Science, vol. 8, no. 12, May 2021.
A. G. Howard et al., “MobileNets: Efficient convolutional neural networks for mobile vision applications,” arXiv, Apr. 2017.
M. Sandler, A. Howard, M. Zhu, A. Zhmoginov and L. -C. Chen, “MobileNetV2: Inverted residuals and linear bottlenecks,” 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, 2018, pp. 4510–4520.
M. Tan, and Q. V. Le., “EfficientNet: Rethinking model scaling for convolutional neural networks,” in Proceedings of the 36th International Conference on Machine Learning, pp. 6105–6114, Long Beach, California, 2019.
F. N. Iandola, S. Han, M. W. Moskewicz, K. Ashraf, W. J. Dally, and K. Keutzer, “SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size,” arXiv, Feb. 2016.
K. He, X. Zhang, S. Ren and J. Sun, “Deep residual learning for image recognition,” 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, 2016, pp. 770–778.
X. Zhang, X. Zhou, M. Lin and J. Sun, “ShuffleNet: An extremely efficient convolutional neural network for mobile devices,” 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, 2018, pp. 6848–6856.
S. Han, H. Mao, and W. J. Dally, “Deep compression: Compressing deep neural networks with pruning, trained quantization and Huffman coding,” arXiv, Oct. 2015.
G. Hinton, O. Vinyals, and J. Dean, “Distilling the knowledge in a neural network,” arXiv, 2015.
R. Qamar, R. Asif, and S. M. Jameel, “Towards sustainable AI: Benchmarking energy efficiency of deep neural networks for resource-constrained edge devices,” Information, vol. 17, no. 4, Apr. 2026.
