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

Authors :
Peng, Huiyun
Gupte, Arjun
Eliopoulos, Nicholas John
Ho, Chien Chou
Mantri, Rishi
Deng, Leo
Jiang, Wenxin
Lu, Yung-Hsiang
Läufer, Konstantin
Thiruvathukal, George K.
Davis, James C.
Publication Year :
2024

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.

Details

Database :
arXiv
Publication Type :
Report
Accession number :
edsarx.2410.09241
Document Type :
Working Paper