Multiprocessor System-on-Chip (MPSoC) is becoming a major VLSI system design platform due to its advantages in low design cost and high performance. However, power consumption in MPSoC is a crucial factor that is limiting the growth of system performance and functionality. The complexity of the MPSoC hardware and software imposes new challenges and requirements for research in system-level power management. An effective power manager must be aware of the status of the hardware, the application and the working environment and be able to adapt to the changes. It should be able to work robustly even if the perfect system information is not available. As the number of components that can be power controlled increases, it is increasingly difficult to perform power management in a centralized manner. A hierarchical and distributed power management method is more suitable for MPSoC platforms. Finally, resource management, power management, and thermal management are inter-correlated tasks and it is desirable for them to be optimized simultaneously. This research project addresses the above mentioned challenges by investigating the theoretical foundation and the applied framework of adaptive power management for the next generation MPSoC. This project consists of four research components: (1) investigate online modeling techniques for runtime workload prediction and hardware performance/power characterization; (2) research new optimization techniques for adaptive resource and power management in a partially observable system; (3) model the distributed power management problem as a multi-agent cooperative game and develop control policy using game theory; (4) develop a unified and standard platform for modeling, optimization and evaluation of power-managed MPSoC. The educational components of this project will introduce the students to the implementation and optimization techniques of system-level power management and provide students unique hands-on experience with MPSoC design and optimization.
