Variation-aware evaluation of MPSoC task allocation and scheduling strategies using statistical model checking

To maximize the overall performance yield, variation-aware analysis is becoming a key step in Multiprocessor System-on-Chip (MP-SoC) Task Allocation and Scheduling (TAS). Although various approaches have been investigated to improve performance yields, most of them cannot perform quantitative comparison among existing TAS heuristics, which is important for MPSoC designers to make decisions. Based on the statistical model checker UPPAAL-SMC, we propose a framework that can automatically evaluate the performance yield of TAS strategies under time and power constraints with variations. Experimental results show that our approach can not only filter inferior strategies efficiently, but also support the automated tuning of architecture and constraint parameters to achieve the required performance yield.