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This work presents an intraprocess dynamic voltage and frequency scaling (DVFS) technique targeted toward nonreal-time applications running on an embedded system platform. The key idea is to make use of runtime information about the external memory access statistics in order to perform CPU voltage and frequency scaling with the goal of minimizing the energy consumption while translucently controlling the performance penalty. The proposed DVFS technique relies on dynamically constructed regression models that allow the CPU to calculate the expected workload and slack time for the next time slot and, thus, adjust its voltage and frequency in order to save energy, while meeting soft timing constraints. This is, in turn, achieved by estimating and exploiting the ratio of the total off-chip access time to the total on-chip computation time. The proposed technique has been implemented on an XScale-based embedded system platform and actual energy savings have been calculated by current measurements in hardware. For memory-bound programs, a CPU energy saving of more than 70% with a performance degradation of 12% was achieved. For CPU-bound programs, 15% ∼ 60% CPU energy saving was achieved at the cost of 5%-20% performance penalty.