Abstract
Low-power design for various applications has always been a challenge for system designers. Dynamic power management, by selectively shutting down idle components, is widely studied and considered to be effective in reducing power consumption. Management strategies based on online algorithm exhibit a feature of easy implementation and fast processing speed. However, merely based on the historical distribution of idle periods, these strategies will make inaccurate prediction if the real distribution of idle periods changes sharply. This paper presents an optimized adaptive dynamic power management for further power saving. We introduce a differential adjusting factor to optimize the exponential-average algorithm to rapidly and accurately adjust the predicted idle period to the real distribution. Experimenting results demonstrate that our policy of power management can reduce the power dissipation of processors in a larger scale and be utilized in diverse applications.
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