Data-Centric Green AI: An Exploratory Empirical Study

TL;DR

This study empirically investigates how data-centric modifications, such as changing dataset size and features, can significantly reduce AI energy consumption with minimal impact on accuracy, highlighting a promising avenue for Green AI.

Contribution

It provides the first empirical evidence that dataset modifications alone can drastically reduce AI energy consumption, emphasizing the importance of data-centric approaches for Green AI.

Findings

Dataset modifications can reduce energy consumption by up to 92.16%.

Changing algorithms can lead to energy savings up to two orders of magnitude.

Data-centric techniques are crucial for advancing Green AI.

Abstract

With the growing availability of large-scale datasets, and the popularization of affordable storage and computational capabilities, the energy consumed by AI is becoming a growing concern. To address this issue, in recent years, studies have focused on demonstrating how AI energy efficiency can be improved by tuning the model training strategy. Nevertheless, how modifications applied to datasets can impact the energy consumption of AI is still an open question. To fill this gap, in this exploratory study, we evaluate if data-centric approaches can be utilized to improve AI energy efficiency. To achieve our goal, we conduct an empirical experiment, executed by considering 6 different AI algorithms, a dataset comprising 5,574 data points, and two dataset modifications (number of data points and number of features). Our results show evidence that, by exclusively conducting modifications on datasets, energy consumption can be drastically reduced (up to 92.16%), often at the cost of a negligible or even absent accuracy decline. As additional introductory results, we demonstrate how, by exclusively changing the algorithm used, energy savings up to two orders of magnitude can be achieved. In conclusion, this exploratory investigation empirically demonstrates the importance of applying data-centric techniques to improve AI energy efficiency. Our results call for a research agenda that focuses on data-centric techniques, to further enable and democratize Green AI.