Control-theoretic dynamic voltage scaling for embedded controllers

For microprocessors used in real-time embedded systems, minimizing power consumption is difficult due to the timing constraints. Dynamic voltage scaling (DVS) has been incorporated into modern microprocessors as a promising technique for exploring the trade-off between energy consumption and system performance. However, it remains a challenge to realize the potential of DVS in unpredictable environments where the system workload cannot be accurately known. Addressing system-level power-aware design for DVS-enabled embedded controllers, this paper establishes an analytical model for the DVS system that encompasses multiple real-time control tasks. From this model, a feedback control based approach to power management is developed to reduce dynamic power consumption while achieving good application performance. With this approach, the unpredictability and variability of task execution times can be attacked. Thanks to the use of feedback control theory, predictable performance of the DVS system is achieved, which is favorable to real-time applications. Extensive simulations are conducted to evaluate the performance of the proposed approach.Comment: Accepted for publication in IET Computers and Digital Techniques. doi:10.1049/iet-cdt:2007011

A Control Theoretic Approach to Run-Time Energy Optimization of Pipelined Processing in MPSoCs

In this work we take a control-theoretic approach to feedbackbased dynamic voltage scaling (DVS) in Multi Processor System on Chip (MPSoC) pipelined architectures. We present and discuss a novel feedback approach based on both linear and non-linear techniques aimed at controlling interprocessor queue occupancy. Theoretical analysis and experiments, carried out on a cycleaccurate multiprocessor simulation platform, show that feedbackbased control reduces energy consumption with respect to standard local DVS policies and highlight that non-linear strategies allows a more flexible and robust implementation in presence of variable workload conditions. 1