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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

TL;DR

The paper investigates using large language models to optimize energy consumption in software by refactoring code while preserving semantics. It introduces an automated, energy-aware refactoring pipeline with Energy-Aware Prompting (EAP) and Energy Optimization Evaluation (EOE) and evaluates it using the Energy-Language benchmark, reporting energy reductions in several programs and highlighting improvements in memory traffic and vectorization. Key contributions include a practical prototype combining generator and evaluator LLMs and a discussion of challenges such as energy-variance and run-time errors, along with a concrete research agenda. The work demonstrates a promising direction for integrating energy considerations into software engineering practice and lays out steps for broader validation and refinement. Overall, it provides a foundation for energy-focused optimization using LLMs and emphasizes a need for multi-objective and hardware-aware approaches.

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.

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

TL;DR

The paper investigates using large language models to optimize energy consumption in software by refactoring code while preserving semantics. It introduces an automated, energy-aware refactoring pipeline with Energy-Aware Prompting (EAP) and Energy Optimization Evaluation (EOE) and evaluates it using the Energy-Language benchmark, reporting energy reductions in several programs and highlighting improvements in memory traffic and vectorization. Key contributions include a practical prototype combining generator and evaluator LLMs and a discussion of challenges such as energy-variance and run-time errors, along with a concrete research agenda. The work demonstrates a promising direction for integrating energy considerations into software engineering practice and lays out steps for broader validation and refinement. Overall, it provides a foundation for energy-focused optimization using LLMs and emphasizes a need for multi-objective and hardware-aware approaches.

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.

Paper Structure

This paper contains 14 sections, 2 figures, 1 table.

Table of Contents

  1. Introduction
  2. Background and Related Works
  3. Energy-efficient Computing and Software
  4. LLM-Driven Code Generation and Optimization
  5. Prototype: Design, Implementation, & Eval.
  6. Design
  7. Prototype Implementation
  8. Experimental Setup
  9. Preliminary Results and Analysis
  10. Future Plans
  11. Improvements to Evaluation
  12. Prototype Framework Refinements
  13. Broader Research Directions
  14. Conclusion

Figures (2)

  • Figure 1: Standard optimization prompts focus on speed, memory optimization, and code readability. Our proposed energy optimization prompts focus on reducing energy usage. One must then ensure that optimizations actually reduce energy use, not just performance metrics such as latency and FLOPS.
  • Figure 2: Overview of our prototype. The core components of the automated feedback loop are Energy-Aware Prompting (EAP) and Energy Optimization Evaluation (EOE).