Abstract-Design variability due to within-die and die-to-die variations has potential to significantly reduce the maximum operating frequency and effective performance of the system in future process technology generations. When multiple cores in MPSoC have different delay distributions, the problem of assigning tasks to the cores become challenging. This paper targets system level task allocation to stochastically minimize the total execution time of an application on MPSoC under process variation. In this work, we first introduce stochastically optimal task allocation problem. We provide formal theorems of the optimality of the solution in simple scenarios. We extend our theoretical work for generic cases in normal distribution. The proposed techniques enable efficient computation of task allocation using non-stochastic analysis. We apply these techniques in allocating tasks in the embedded system benchmark suites on MPSoC. We show that deterministic solution for system-level task allocation on widely used benchmark topologies and distributions (normal distribution) is almost as good as the best probabilistic solution.

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Battery lifetime, a primary design constraint for mobile embedded systems, has been shown to depend heavily on the load current profile. This paper explores how scheduling guidelines from battery models can help in extending battery capacity. It then presents a 'Battery-Aware Scheduling' methodology for periodically arriving taskgraphs with real time deadlines and precedence constraints. Scheduling of even a single taskgraph while minimizing the weighted sum of a cost function has been shown to be NP-Hard [6]. The presented methodology divides the problem in to two steps. First, a good DVS algorithms dynamically determines the minimum frequency of execution. Then, a greedy algorithm allows a near optimal priority function [5] to choose the task which would maximize slack recovery. The methodology also ensures adherence of real time deadlines independent of the choice of the DVS algorithm and priority function used, while following battery guidelines to maximize battery lifetime. Battery simulations carried out on the profile generated by our methodology for a large set of taskgraphs show that battery life time is extended up to 23.3% as compared to existing dynamic scheduling schemes.

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