Green AI: Balancing Model Complexity and Energy Footprint in Deep Learning

The rapid advancement of deep learning has led to significant breakthroughs in various domains, including computer vision, natural language processing, and autonomous systems. However, the growing complexity and size of these models come with a significant environmental cost. Training state-of-the-art models consumes enormous computational power, contributing to high energy consumption and carbon emissions. This paper explores the paradigm of Green AI, emphasizing the need to balance model complexity and performance with environmental sustainability. We analyse the environmental impact of deep learning models, review recent strategies for energy-efficient AI, and propose a research framework to guide sustainable model development. The study also presents research objectives, a methodology for empirical evaluation, and a discussion of findings aimed at guiding researchers toward greener practices.