Learning-Based Power/Performance Optimization for Many-Core Systems with Extended-Range Voltage/Frequency Scaling

Near-threshold computing has emerged as a promising solution to significantly increase the energy efficiency of next-generation multicore systems. This paper evaluates and analyzes the behavior of dynamic voltage and frequency scaling for multicore systems operating under extended range: including near-threshold, nominal, and turbo modes. We adapt the model selection technique from machine learning to determine the relationship between performance and power. The theoretical results show that the resulting models satisfy convexity, which efficiently determines the optimal voltage/frequency operating points for: 1) minimizing energy consumption under throughput constraints or 2) maximizing throughput under a given power budget. We validate our models on FinFET-based chip-multiprocessors. Considering process variations (PVs), experimental results show that at 30% PV levels, our proposed method: 1) reduces energy consumption by 31.09% at iso-performance condition and 2) increases throughput by 11.46% at iso-power when compared with variation-agnostic nominal case.