Energy-Aware Machine Learning Models—A Review of Recent Techniques and Perspectives

Rafał Różycki (), Dorota Agnieszka Solarska and Grzegorz Waligóra
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Rafał Różycki: Institute of Computing Science, Poznan University of Technology, Piotrowo 2, 60-965 Poznan, Poland
Dorota Agnieszka Solarska: Institute of Computing Science, Poznan University of Technology, Piotrowo 2, 60-965 Poznan, Poland
Grzegorz Waligóra: Institute of Computing Science, Poznan University of Technology, Piotrowo 2, 60-965 Poznan, Poland

Energies, 2025, vol. 18, issue 11, 1-33

Abstract: The paper explores the pressing issue of energy consumption in machine learning (ML) models and their environmental footprint. As ML technologies, especially large-scale models, continue to surge in popularity, their escalating energy demands and corresponding CO 2 emissions are drawing critical attention. The article dives into innovative strategies to curb energy use in ML applications without compromising—and often even enhancing—model performance. Key techniques, such as model compression, pruning, quantization, and cutting-edge hardware design, take center stage in the discussion. Beyond operational energy use, the paper spotlights a pivotal yet often overlooked factor: the substantial emissions tied to the production of ML hardware. In many cases, these emissions eclipse those from operational activities, underscoring the immense potential of optimizing manufacturing processes to drive meaningful environmental impact. The narrative reinforces the urgency of relentless advancements in energy efficiency across the IT sector, with machine learning and data science leading the charge. Furthermore, deploying ML to streamline energy use in other domains like industry and transportation amplifies these benefits, creating a ripple effect of positive environmental outcomes. The paper culminates in a compelling call to action: adopt a dual-pronged strategy that tackles both operational energy efficiency and the carbon intensity of hardware production. By embracing this holistic approach, the artificial intelligence (AI) sector can play a transformative role in global sustainability efforts, slashing its carbon footprint and driving momentum toward a greener future.

Keywords: artificial intelligence; machine learning; green AI; energy efficiency; environmental impact (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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