Application partitioning on programmable platforms using the ant colony optimization

Abstract

Modern digital systems consist of a complex mix of computational resources, e.g. microprocessors, memory elements and reconfigurable logic. System partitioning – the division of application tasks onto the system resources – plays an important role for the optimization of the latency, area, power and other performance metrics. This paper presents a novel approach for this problem based on the Ant Colony Optimization, in which a collection of agents cooperate using distributed and local heuristic information to effectively explore the search space. The proposed model can be flexibly extended to fit different design requirements. Experiments show that our algorithm provides robust results that are qualitatively close to the optimal with minor computational cost. Compared with the popularly used simulated annealing approach, the proposed algorithm gives better solutions with substantial reduction on execution time for large problem instances. Moreover, a hybrid approach that combines our algorithm and SA achieves even better results with great runtime reduction.