Optimal DPM and DVFS for frame-based real-time systems
ACM Transactions on Architecture and Code Optimization (TACO) - Special Issue on High-Performance Embedded Architectures and Compilers
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Dynamic Power Management (DPM) is a system level power management technique that selectively shut down idle electrical components to save power. Previous works are mainly focused on certain types of prediction technique which assume that the idle period is long range dependent (heuristic prediction based on past history), random process (Markov Process) or short range dependent (Autoregressive) characteristic. However, the user behavior is highly variable and single assumption might not hold for all conditions. Thus, techniques based on the above assumptions will only be effective in certain condition only. Hence, we propose here a Hybrid Model DPM system that combines Moving Average (MA), Time Delay Neural Network (TDNN) and random walk model to perform idle period prediction. The Hybrid Model will first analyze the Long Range Dependency and central tendency of the past idle period time series, and choose the most appropriate strategy for future idle period prediction. Simulation results show that the Hybrid Model achieves higher power saving in most of the scenarios compared to the other methods.