arXiv Machine Learning
Magneton: Optimizing Energy Efficiency of ML Systems via Differential Energy Debugging
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arXiv AI
Aug 19
Beyond FLOPs: Energy-Aware Knowledge Distillation for Sustainable LLMs on Code-Related Task
The paper explores energy-aware knowledge distillation for large language models (LLMs) used in software engineering tasks such as clone detection, vulnerability prediction, and code summarization. It shows that the commonly used FLOPs metric does not reliably reflect actual energy consumption, and that using energy-surrogate models during distillation can reduce inference energy by up to 90% and memory usage by 86% with only modest accuracy loss. The study demonstrates that guiding distillation with direct energy estimates improves the sustainability and deployability of LLMs on consumer hardware.
Hugging Face Trending Papers
Jul 6
Beyond the Need for Speed: Energy-Aware Code Generation via Simulation-Guided Reinforcement Learning
Code models strictly prioritize functional correctness, leaving software energy efficiency as an unoptimized byproduct. Training models to generate energy-efficient code requires reproducible feedback at scale, which physical hardware measurement cannot reliably provide due to variance.
arXiv AI
Jul 8
Static Metrics Are Insufficient: Predicting Java Method Energy Usage with Execution Time
arXiv:2607. 06124v1 Announce Type: cross Abstract: The increasing energy demand of software systems is raising concerns about their environmental impact and associated costs.
arXiv Machine Learning
Jul 7
Beyond the Need for Speed: Energy-Aware Code Generation via Simulation-Guided Reinforcement Learning
arXiv:2607. 04577v1 Announce Type: new Abstract: Code models strictly prioritize functional correctness, leaving software energy efficiency as an unoptimized byproduct.
arXiv AI
Oct 5
Improving the Energy-Efficiency of the Code Generated by LLMs through Effective Prompting
arXiv:2610.02571v1 Announce Type: cross Abstract: As AI-assisted programming becomes increasingly mainstream, the environmental impact of AI-generated software has emerged as an important considerati...
arXiv AI
Oct 15
Measuring the Inference Energy Efficiency of Open-Weight LLMs across Benchmarks, Reasoning Modes, and Hardware
arXiv:2610.14389v1 Announce Type: new Abstract: Benchmark scores tell us what an LLM can do, but not how much energy is required to deliver that capability. We directly measure this trade-off for 86...