Green AI: Energy-Efficient Machine Learning Models for Sustainable Computing

Abstract

Artificial Intelligence (AI) has emerged as one of the most game-changers in today's world of innovation, revolutionizing the healthcare, finance, manufacturing, transportation, education, and smart city sectors. However, the exponential expansion of machine learning (ML) models, particularly deep neural networks and large language models, has led to significant rise in both computational complexity, electricity consumption and carbon emissions during training and deployment of these models. Due to this concern, there is a new paradigm of research on energy-efficient model design, computational optimization, and environmentally sustainable AI development, known as Green AI. In this paper, the authors provide an overview and a framework for designing energy-efficient machine learning models for sustainable computing infrastructures. The proposed Green AI framework aims to lower the computational overhead by utilizing efficient data preprocessing, light hardware, compression and pruning of neural networks, knowledge distillation, efficient hardware utilization, renewable energy-aware scheduling, and carbon-aware optimization to preserve high accuracy of learning. The study also covers metrics for evaluating model performance, energy consumption models, and indicators of sustainability that allow researchers to quantify environmental impacts throughout the creation of AI models. In addition, the paper outlines current research challenges such as the need for accuracy and efficiency, energy-evaluation approaches, and environmentally friendly AI systems. The overall framework outlines the potential impact of Green AI in areas such as cost reduction, carbon footprint minimization, resource efficiency, and sustainable digital transformation, making it a promising avenue for advancing the sustainability of computing systems.