Green Artificial Intelligence and Quantization in Deep Learning Models

• Eduardo F. Gonçalves UNIRIO
• Pedro Nuno de S. Moura UNIRIO
• Daniel da S. Costa UNIRIO
• Adriana C. F. Alvim UNIRIO

DOI: https://doi.org/10.5753/wcama.2025.8923

Abstract

Concerns about the environment are playing an increasingly important role in society. Artificial Intelligence (AI), through Deep Learning (DL), offers useful tools for dealing with environmental issues. Despite the growing potential of DL models, they often require a lot of computing power, resulting in high levels of energy consumption and carbon footprint. Green AI aims to develop DL models that balance energy efficiency and performance in order to reduce environmental impact. This work focuses on quantization as a way of achieving the goals of Green AI, comparing how mixed-precision quantization impacts the energy consumption and performance of some neural networks for computer vision. The experimental results show that quantization can lead to the construction of more energy-efficient yet effective models, promoting a more sustainable approach.

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