SmartBoost: Lightweight ML-Driven Boosting for Thermally-Constrained Many-Core Processors

Dynamic voltage and frequency scaling (DVFS)-based boosting is indispensable for optimizing the performance of thermally-constrained many-core processors. State-of-the-art techniques employ the voltage/frequency (V/D sensitivity of the performance of an application as a boosting metric. This paper demonstrates that this leads to suboptimal boosting decisions because the sensitivities of power and temperature also play a profound impact and need to be included within the optimization. Therefore, we introduce a novel boosting metric that integrates all relevant metrics: the application-dependent V/f sensitivities of performance and power, and the core-dependent sensitivity of the temperature. This new boosting metric is derived at run-time using machine learning via a neural network (NN) model, which accurately estimates the V/f sensitivities of performance and power of a priori unknown applications with diverse and time-varying characteristics. This new metric enables to build a smart, yet lightweight, boosting technique to maximize the performance under a temperature constraint. The experimental results demonstrate a 21 % average improvement of the system performance over the state-of-the-art at a negligible run-time overhead of 0.8 %.