Efficient temporal task partition for coarse-grain reconfigurable systems based on Simulated Annealing Genetic Algorithm

A Simulated Annealing Genetic Algorithm (SAGA) with greedy mapping mechanism is developed to solve task partitioning problems in coarse grain reconfigurable systems. A fitness function combined multiple objectives (communication cost, number of partitions and number of bypass nodes) is constructed to optimize the execution time. Experimental results show that SAGA produces better solutions than traditional level-based or clustering-based partitioning algorithm. The operation time saved is up to 5% compared with level-based algorithm, and critical parameters such as communication cost and number of partitions are reduced by 10% in average.