Green/Energy-Efficient Ai: Strategies for Reducing Power Usage in Large-Scale Model Training

The fast pace of progress in AI and deep learning has made paradigm-shifting developments across various domains, including healthcare diagnostics and natural language processing. But all these advances come at a considerable cost to nature. Training these large models such as GPT-3, BERT, and transformer-based architectures require enormous computational resources and thereby draw huge power consumptions and carbon emissions into the environment. Power consumption measured in megawatt-hours for merely one cycle of training with a state-of-the-art model only adds to the existing carbon footprints of AI systems. Hence, the emergence of green training, a discipline developed to cater to the diminution of the environmental costs of AI by emphasizing energy-efficient training methods. We present case studies that highlight real-life instances of green training in production environments by tech giants such as Google, Meta, and Hugging Face. We finish with policy recommendations for academia, industry, and regulators designed to promote transparency around metrics, incentivize sustainable behavior, and eventually integrate environmental metrics into an AI ethics framework.