Normalizing Energy Consumption for Hardware-Independent Evaluation

TL;DR

This paper introduces a new method to normalize energy consumption measurements across various hardware platforms, enabling fairer comparisons of ML training energy use and promoting sustainable AI development.

Contribution

It proposes a novel normalization methodology that accounts for hardware differences and includes computational metrics to improve energy consumption evaluation accuracy.

Findings

Two reference points provide robust normalization.

Including FLOPs and parameters enhances prediction accuracy.

Normalization method supports environmentally sustainable ML practices.

Abstract

The increasing use of machine learning (ML) models in signal processing has raised concerns about their environmental impact, particularly during resource-intensive training phases. In this study, we present a novel methodology for normalizing energy consumption across different hardware platforms to facilitate fair and consistent comparisons. We evaluate different normalization strategies by measuring the energy used to train different ML architectures on different GPUs, focusing on audio tagging tasks. Our approach shows that the number of reference points, the type of regression and the inclusion of computational metrics significantly influences the normalization process. We find that the appropriate selection of two reference points provides robust normalization, while incorporating the number of floating-point operations and parameters improves the accuracy of energy consumption predictions. By supporting more accurate energy consumption evaluation, our methodology promotes the development of environmentally sustainable ML practices.