Hardware/Software Partitioning Algorithm under Multi-Constraints for the Optimization of Power Consumption

An improved particle swarm algorithm with multiple neighborhood optimizations was proposed for hardware/software partitioning of system on chip under multiple constraints. A method for fitness calculation was designed with the unity of multiple constraint conditions and objective function. The particle position was updated by the average of the best position for all particles, the individual optimal location and the global optimal position. The effect of the average of position on particle position was adjusted adaptively according to the number of iterations. The multiple neighborhood of the optimal solution was searched for a better solution. The variation information of the optimal position was randomly produced by the Gaussian function. An arbitrary particle in population would be replaced by the variation particle whose fitness was better than the optimal fitness. The experimental results show that the proposed algorithm achieves hardware/software partitioning with lower power in a shorter searching time under the same constraints.