A meta heuristic-based task scheduling and mapping method to optimize main design challenges of heterogeneous multiprocessor embedded systems

In this paper, we proposed a meta heuristic-based task scheduling method to optimize lifetime reliability, performance and power consumption of heterogeneous MPSoCs. Lifetime reliability is affected by several failure mechanisms with different behaviors which are mainly dependent on temperature and its variation pattern. To improve lifetime reliability of multiprocessor systems, it is required to consider the effect of all potential failures and their distinct impact during the optimization process. Moreover, improving power consumption and execution time makes the optimization process more complicated due to the existing trade offs among these parameters. Our proposed task scheduling method optimizes lifetime reliability by considering the effect of all failure mechanisms, power consumption and execution time of heterogeneous MPSoCs. It employs a design space exploration engine based on the Non-dominated Sorting Genetic Algorithm (NSGA-II) to make the exploration process more efficient. To demonstrate the effectiveness of our proposed task scheduling and mapping method and compare it to the related studies, several experiments are performed. Moreover, the importance of thermal cycling (TC), as an emerging thermal concern in computing the lifetime reliability of MPSoCs, and also the capability of our proposed method in controlling it are studied and compared to related research. Experimental results show that employing our proposed scheduling method improves performance, lifetime reliability and power consumption about 24%, 30% and 3.6% respectively on average compared to two selected related studies. Furthermore, our proposed approach decreases the occurrence rate of all failure mechanisms compared to related studies and outperforms them in term of the thermal cycling rate about 48% on average.