A Learning-Based DVFS Power Management Scheme for Multicore Processors

Reducing the size of transistors in multi-core processors has caused reduced energy consumption, fixed power density, and exponential growth in performance. In new generations of integrated circuits, despite the smaller transistors, the energy consumption has not been scaled down anymore; therefore, with constant power consumption in each transistor, the increasing number of transistors leads to an exponential growth in total power consumption, thermal concerns and dark silicon problems. In addition, the effects of aging are essential for the design and construction of integrated circuits. Since aging reduces the service lifetime of a circuit, this deterioration can affect all aspects including performance and reliability. Dynamic voltage and frequency scaling are power management methods used to control the workload. This paper presents a supervised learning-based method that predicts the performance of the system through some available input features and then adjusts the appropriate frequency and voltage for each workload. Simulation results show 95% accuracy in a multi-core processor using the decision tree method.