Large Language Models for Energy-Efficient Code: Emerging Results and Future Directions

Huiyun Peng; Arjun Gupte; Nicholas John Eliopoulos; Chien Chou Ho; Rishi Mantri; Leo Deng; Wenxin Jiang; Yung-Hsiang Lu; Konstantin Läufer; George K. Thiruvathukal; James C. Davis

arXiv · 2410.09241

Large Language Models for Energy-Efficient Code: Emerging Results and Future Directions

Huiyun Peng·Arjun Gupte·Nicholas John Eliopoulos·Chien Chou Ho·Rishi Mantri·Leo Deng·Wenxin Jiang·Yung-Hsiang Lu·Konstantin Läufer·George K. Thiruvathukal·James C. Davis

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Abstract

Energy-efficient software helps improve mobile device experiences and reduce the carbon footprint of data centers. However, energy goals are often de-prioritized in order to meet other requirements. We take inspiration from recent work exploring the use of large language models (LLMs) for different software engineering activities. We propose a novel application of LLMs: as code optimizers for energy efficiency. We describe and evaluate a prototype, finding that over 6 small programs our system can improve energy efficiency in 3 of them, up to 2x better than compiler optimizations alone. From our experience, we identify some of the challenges of energy-efficient LLM code optimization and propose a research agenda.

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Huiyun Peng, Arjun Gupte, Nicholas John Eliopoulos, Chien Chou Ho, Rishi Mantri, Leo Deng, Wenxin Jiang, Yung-Hsiang Lu, Konstantin Läufer, George K. Thiruvathukal, James C. Davis. 2024-10-11. Large Language Models for Energy-Efficient Code: Emerging Results and Future Directions. https://arxiv.org/abs/2410.09241

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