Uncertain Model and Algorithm for Hardware/Software Partitioning

Embedded systems are becoming increasingly popular due to their widespread applications. Hardware/software partitioning is becoming one of the most crucial steps in the design of embedded systems. The costs and delays of the final results of a design will strongly depend on partitioning. In this paper, we propose an uncertain programming model for partitioning problems. The delay related constraints and the cost related objective are modeled by uncertain variables with uncertainty distributions. We convert the uncertain programming model to a deterministic model and solve the converted model by an efficient heuristic method. We propose a heuristic based on genetic algorithm and simulated annealing to solve the problem near-optimally, even for quite large systems. Experiment results show that the proposed model and algorithm produce quality partitions.