A Beginner’s Guide to Power and Energy Measurement and Estimation for Computing and Machine Learning

We teamed up with Intel to create A Beginner’s Guide to Power and Energy Measurement and Estimation for Computing and Machine Learning. This in-depth guide equips AI developers and other software professionals with the skills to make intelligent measurement decisions — from deciding at-the-wall versus on-device measurements, sampling strategies, where to look for errors, and when proxy measures are sufficient. These are vital first steps in pinpointing which optimizations and model choices have the greatest impact on sustainability.

Media

• Blog Post from Intel
• Podcast Appearance on Environment Variables

Abstract

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.