Leveraging Machine Learning for Sustainable Microprocessor Utilization

This paper examines the integration of machine learning advancements with microprocessor hardware to enhance speed and energy efficiency. It reviews recent studies and promising approaches addressing existing challenges. Beyond this primary focus, the paper explores related technologies such as quantum computing, neuromorphic computing, and 3D stacking, as well as emerging fields like photonic computing, highlighting their potential synergies with machine learning to further improve microprocessor performance.