DOI: 10.7763/IJCTE.2026.V18.1397
A Decision-support Framework for Energy-efficient Batch Optimization in Machine Learning
2. Department of Mechanical Engineering, Lead City University, Off Oba Otudeko Avenue, Lagos-Ibadan Express Way Toll Gate Area, Oyo, Ibadan 200255, Nigeria
3. Department of Electrical Computer Engineering, NUS (Suzhou) Research Institute, Suzhou, China
4. School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing, China
5. School of Law and Public Administration, Research Institute of History Science and Technology, University of Information Science and Technology, Nanjing, China
Email: adeyi.timothy@lcu.edu.ng (T.A.A.); adrian@imagineeringinstitute.org (A.D.C.); songhuihui@nuist.edu.cn (H.S.); elezzy@nus.edu.sg (Z.Y.Z.); emma@imagineeringinstitute.org (E.Y.Z.); wpp@ucsc.cmb.ac.lk (W.P.P.); quistishmeal@nuist.edu.cn (I.Q.)
*Corresponding author
Manuscript received September 25, 2025; revised December 21, 2025; accepted March 20, 2026; published July 17, 2026
Abstract—This paper presents a decision-support framework for selecting energy-efficient batch optimization techniques in machine learning, addressing the absence of reproducible, theory-informed guidance in Green Artificial Intelligence (AI). Eight key methods, including Mixed Precision Training, Dynamic Batching, and Gradient Accumulation are systematically evaluated through a rigorous systematic literature review and assessed across five criteria: energy efficiency, accuracy impact, scalability, technological maturity, and hardware dependency. We operationalize a transparent, weighted Multi-Criteria Decision Analysis (MCDA) protocol, termed the Green AI Score, and a normalized efficiency metric, Energy-Delay Normalized Accuracy (EDNA). These instruments serve as a reproducible analytical scaffold to enable cross-technique comparison and quantification of energy-accuracy trade-offs. Advanced visual analytics, including a heatmap, dual-axis bar chart, bubble plot, and correlation visualization, link sustainability rankings with energy-accuracy trade-offs. Our analysis identifies Mixed Precision Training (Score: 0.84) and Dynamic Batching (Score: 0.77) as top-tier strategies, offering 40–60% and 30–50% energy savings, respectively, with minimal accuracy loss. Furthermore, we theoretically derive a hardware-aware heuristic threshold (memory-bandwidth-to-compute ratio < 0.4) to analytically assess the applicability of Mixed Precision Training under varying system constraints. Finally, a rule-based decision policy is synthesized to support informed technique selection, providing practitioners with a theoretically grounded guide for navigating deployment trade-offs. This work advances beyond descriptive surveying by providing an evidence-based, reproducible analytical synthesis grounded in systems and algorithmic considerations, thereby supporting transparent and sustainable AI deployment.
Keywords—energy efficiency, Multi-Criteria Decision Analysis (MCDA), Green AI, systematic evaluation, sustainable AI deployment
Cite: Timothy A. Adeyi, Adrian D. Cheok, Huihui Song, Zhiying Y. Zhou, Emma Y. Zhang, Wisura P. Pallewatta, and Ishmeal Quist, "A Decision-support Framework for Energy-efficient Batch Optimization in Machine Learning," International Journal of Computer Theory and Engineering, vol. 18, no. 3, pp. 154-163, 2026.
Copyright © 2026 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).