Mobile SoC Power Optimization: Redefining Performance with Machine Learning Techniques

Power optimization is a critical challenge in System-on-chip (SoC) design due to the growing demand for high-performance mobile devices with extended battery life. This paper explores novel applications of machine learning (ML) technologies to readdress the question of power optimization strategies in mobile SoCs. In order to balance performance and power consumption in real time, ML takes advantage of predictive modeling, dynamic power management, and energy efficient workload scheduling. This study makes key contributions to integrating ML models into SoC architectures, including a novel ML-driven adaptive voltage scaling and frequency tuning workflow that demonstrates the potential for significant power savings, as well as empirical analysis. Given our findings, we believe that ML offers a transformative potential for the realization of sustainable and efficient mobile computing solutions.