Data-Centric Green AI: An Exploratory Empirical Study
- URL: http://arxiv.org/abs/2204.02766v2
- Date: Thu, 7 Apr 2022 08:21:14 GMT
- Title: Data-Centric Green AI: An Exploratory Empirical Study
- Authors: Roberto Verdecchia, Lu\'is Cruz, June Sallou, Michelle Lin, James
Wickenden, Estelle Hotellier
- Abstract summary: We investigate the impact of data-centric approaches on AI energy efficiency.
Our results show evidence that, by exclusively conducting modifications on datasets, energy consumption can be drastically reduced.
Our results call for a research agenda that focuses on data-centric techniques to further enable and democratize Green AI.
- Score: 6.4265933507484
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
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