Dynamic Voltage Frequency Scaling in Multi-core Systems using Adaptive Regression Model

A learning-based manager that controls the power budget through dynamic voltage frequency scaling (DVFS) in a multi-core processor has been proposed in this paper. The core statistics are collected and employed to predict the next interval power consumption and are thereby used to determine the best suited voltage-frequency setting for each core. The aim is to maximize perforformance while containing the power consumption per-core. The presented solution is realized in Snipersim and the fine-grained DVFS algorithm is included through Python scripting. Simulation results demonstrate that the proposed approach is able to achieve 6.6 % energy-reduction and average power-savings of 27.4% against the existing state-of-the-art algorithm (Steepest Drop) for various allocation schemes.