Imitation Learning for Dynamic VFI Control in Large-Scale Manycore Systems

Abstract

Manycore chips are widely employed in high-performance computing and large-scale data analysis. However, the design of high-performance manycore chips is dominated by power and thermal constraints. In this respect, voltage–frequency island (VFI) is a promising design paradigm to create scalable energy-efficient platforms. By dynamically tailoring the voltage and frequency of each island, we can further improve the energy savings within given performance constraints. Inspired by the recent success of imitation learning (IL) in many application domains and its significant advantages over reinforcement learning (RL), we propose the first architecture-independent IL-based methodology for dynamic VFI (DVFI) control in manycore systems. Due to its popularity in the EDA community, we consider an RL-based DVFI control methodology as a strong baseline. Our experimental results demonstrate that IL is able to obtain higher quality policies than RL (on average, 5% less energy with the same level of performance) with significantly less computation time and hardware area overheads (3.1X and 8.8X, respectively).