Deep Learning and Green Artificial Intelligence: Pathways to a More Sustainable Future

• Vívian R. G. Ferraro UNIRIO
• Gabriel Gullo UNIRIO
• Daniel da Silva Costa UNIRIO
• Pedro Nuno de S. Moura UNIRIO

DOI: https://doi.org/10.5753/wcama.2024.3033

Abstract

In the last decade, there have been significant advances in the results achieved by Deep Learning models and their widespread adoption in academia and industry. Although these models have the potential to assist in the management of natural resources and environmental issues, they typically require a great deal of computing power, resulting in higher energy costs and also large carbon footprint numbers. This work seeks to highlight and discuss the energy costs involved in using neural network models, experimentally comparing some architectures in terms of performance, energy efficiency, and computational cost. The results obtained reinforce that it is possible to build models that consume less energy and have performance compatible with more expensive ones, contributing to a more sustainable approach.

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