AI-driven dynamic power allocation between CPU and GPU for optimal performance and battery life

Dynamic power allocation strategies for heterogeneous computing systems have emerged as a crucial advancement in optimizing AI workload performance while managing energy consumption. This article explores the fundamental challenges posed by static power allocation in CPU-GPU systems and presents AI-driven solutions that enable intelligent redistribution of power resources based on real-time computational demands. The integration of machine learning techniques for workload characterization and power prediction allows these systems to anticipate phase-dependent behavior and proactively adjust power distribution, significantly improving both energy efficiency and computational throughput. Various implementation approaches are examined, from hardware-level composable architectures to operating system facilitation mechanisms, highlighting the tangible benefits observed across diverse computing environments from data centers to edge devices. Despite impressive advancements, several challenges persist, including prediction accuracy limitations, implementation complexity, and privacy concerns. Future directions point toward deeper hardware integration of AI capabilities, increasingly granular power control mechanisms, and standardized interfaces across heterogeneous components to further enhance the effectiveness of dynamic power allocation in next-generation computing systems.