fine-grained dynamic voltage and frequency scaling for precise energy and performance trade-off based on the ratio of off-chip access to on-chip computation times
This paper presents an intra-process dynamic voltage and frequency scaling (DVFS) technique targeted toward non real-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.
dynamic scale precise energy performance access computation timing paper real-time applications embedded systems platform use runtime informal memorialized statistics order energy consumption control regression model workload meeting time constraint estimates energy saving measurement hardware program cost process