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.
|Title of host publication||Proceedings of the 2022 International Conference on ICT for Sustainability (ICT4S)|
|Editors||Coral Calero, Andy Karvonen, Elena Somova, Joao Paulo Fernandes, Anne-Kathrin Peters, Jacome Cunha|
|Place of Publication||Piscataway|
|Number of pages||11|
|Publication status||Published - 2022|
|Event||2022 International Conference on ICT for Sustainability (ICT4S) - Plovdiv, Bulgaria|
Duration: 13 Jun 2022 → 17 Jun 2022
|Name||Proceedings - 2022 International Conference on ICT for Sustainability, ICT4S 2022|
|Conference||2022 International Conference on ICT for Sustainability (ICT4S)|
|Period||13/06/22 → 17/06/22|
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Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.
- Energy Efficiency
- Artificial Intelligence
- Green AI