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

TL;DR

This paper provides a comprehensive overview of energy measurement techniques for machine learning, emphasizing best practices, common pitfalls, and the importance of standardization to reduce environmental impact.

Contribution

It offers practical guidance and considerations for accurate energy measurement in ML, highlighting the need for improved standards and methods.

Findings

Guidelines for energy measurement practices

Identification of common measurement errors

Call for standardized measurement methods

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.