A variation and energy aware ILP formulation for task scheduling in MPSoC

In nanometer-scale process technologies, the effects of process variations are observed in Multiprocessor System-on-Chips (MPSoC) in terms of variations in frequencies and leakage powers among the processors on the same chip as well as across different chips of the same design. Traditional approaches try to improve the worst-case value for energy of a system whereas statistical optimizations are more recently employed to optimize the energy yield under a given energy constraint. In this work, we have formulated statistical optimization by integer linear programming. Our experimental results on E3S benchmark suite show that statistical approach for task scheduling can achieve up to 22% improvement over the conventional approach in terms of energy yield and demonstrate this superiority is improved when the amount of variation increases.