1 CSE, Daffodil International University.
2 CSE, City University, Bangladesh.
3 INSTITUTE OF SOCIAL WELFARE AND RESEARCH, UNIVERSITY OF DHAKA.
4 Sabujbagh Govt. College, Dhaka.
5 CSE, Northern University Bangladesh.
International Journal of Science and Research Archive, 2026, 18(01), 287-295
Article DOI: 10.30574/ijsra.2026.18.1.0061
Received on 05 December 2025; revised on 10 January 2026; accepted on 13 January 2026
This paper explores energy-efficient AI techniques that emphasize green computing approaches to achieve sustainable deep learning. It highlights the critical role of optimizing hardware architectures and algorithmic strategies to reduce the environmental impact of AI training and inference, particularly in resource-constrained settings. By integrating advances in low-power AI hardware, approximate computing, and intelligent energy management, this research aims to pave the way for eco-friendly AI solutions that maintain performance while minimizing energy consumption.
Energy-Efficient AI; Green Computing; Sustainable Deep Learning; Low-Power AI Hardware; Eco-Friendly Machine Learning
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Mohammad Quayes Bin Habib, Razibul Islam Khan, MD ABDUR RAHIM, Kazi Wasi Uddin Shad and Muhammad Nesar Uddin. Energy-Efficient AI: Green computing approaches for sustainable deep learning. International Journal of Science and Research Archive, 2026, 18(01), 287-295. Article DOI: https://doi.org/10.30574/ijsra.2026.18.1.0061
Copyright © 2026 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0
