教开发者如何准确测量机器学习能耗，减少碳足迹。
A Beginner's Guide to Power and Energy Measurement and Estimation for Computing and Machine Learning
- 区分墙插与设备端测量，选择合适采样策略。
- 识别常见误差源，避免误判能耗数据。
- 适合关注模型能效与可持续性的研究者。
机器学习的环境影响日益受关注。尽管能耗与排放研究成为新兴领域，大多数ML研究人员和开发者仍未将能耗测量纳入工作流程。准确测量是降低碳足迹的关键，但实际操作复杂。本文介绍了使用能耗测量工具和解读能耗估算时的核心考虑因素，包括墙插式与设备端测量的区别、采样策略与最佳实践、常见误差来源及代理指标。文中还提供实用建议和真实场景案例，说明这些因素如何影响实际应用。最后呼吁改进测量方法与标准，以支持不同软硬件环境间的可靠比较。
原文摘要 · Abstract (English)
Concerns about the environmental footprint of machine learning are increasing. While studies of energy use and emissions of ML models are a growing subfield, most ML researchers and developers still do not incorporate energy measurement as part of their work practices. While measuring energy is a crucial step towards reducing carbon footprint, it is also not straightforward. This paper introduces the main considerations necessary for making sound use of energy measurement tools and interpreting energy estimates, including the use of at-the-wall versus on-device measurements, sampling strategies and best practices, common sources of error, and proxy measures. It also contains practical tips and real-world scenarios that illustrate how these considerations come into play. It concludes with a call to action for improving the state of the art of measurement methods and standards for facilitating robust comparisons between diverse hardware and software environments.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。