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

• URL: http://arxiv.org/abs/2410.09241v1
• Date: Fri, 11 Oct 2024 20:35:40 GMT
• Title: Large Language Models for Energy-Efficient Code: Emerging Results and Future Directions
• Authors: 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,
• Abstract summary: We propose a novel application of large language models (LLMs) as codes 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.
• Score: 2.848398051763324
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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