Don’t burn your mobile! Safe Computational Re-Sprinting via Model Predictive Control

Computational sprinting has been recently proposed as an effective solution to mitigate the upcoming dark silicon uti- lization wall problems. As most applications do not re- quire constantly the maximum performance level and par- allelism, computational sprinting uses the intrinsic thermal capacitance of the heat dissipation system, possibly aug- mented with Phase Change Materials, as heat buffer for tolerating bursts of intensive computational resource usage largely exceeding the steady-state thermal design power. It is clear that sprinting poses peculiar challenges on the dy- namic thermal control policy. In this paper we introduce and evaluate an innovative and low-overhead hierarchical model- predictive controller that enables thermally-safe sprinting while guaranteeing a predictable re-sprinting rate.