System Level Partitioning for Programmable Platforms Using the Ant Colony Optimization

The flexibility, performance and cost effectiveness of programmable platforms have lead to their widespread use for embedded applications. The partitioning of an application tasks onto the different computational cores of a platform is an important step in the mapping of embedded applications onto these systems. This paper presents a novel approach for this problem based on the Ant Colony Optimization. In our algorithm, a collection of agents cooperate to effectively explore the search space. Experimental results show that our algorithm provides robust results that are qualitatively close to optimal with minor computational cost. Compared with the popularly used simulated annealing approach, the proposed algorithm gives better solutions with substantial reduction in execution time.