Energy-Efficient AI: Green computing approaches for sustainable deep learning

This paper explores energy-efficient AI techniques that emphasize green computing approaches to achieve sustainable deep learning. It highlights the critical role of optimizing hardware architectures and algorithmic strategies to reduce the environmental impact of AI training and inference, particularly in resource-constrained settings. By integrating advances in low-power AI hardware, approximate computing, and intelligent energy management, this research aims to pave the way for eco-friendly AI solutions that maintain performance while minimizing energy consumption.