Abstract
Generally, CHardware/Software (HW/SW) partitioning can be approximately resolved through some kinds of optimal algorithms. Based on both characteristics of HW/SW partitioning and Particle Swarm Optimization (PSO) algorithm, a novel parallel HW/SW partitioning method is proposed in this paper. A model of parallel HW/SW partitioning on the basis of PSO algorithm is established after analyzing the particularity of HW/SW partitioning. A hybrid strategy of PSO and Tabu Search (TS) is proposed in this paper, which uses the intrinsic parallelism of PSO and the memory function of TS to speed up and improve the performance of PSO. To settle the problem of premature convergence, the reproduction and crossover operation of genetic algorithm (GA) is also introduced into procedure of PSO. Experimental results indicate that the parallel PSO algorithm can efficiently reduce the running time even for large task graphs.
This work has been Supported by the Shanghai-Applied Materials Research and Development Fund under grant No.06SA18.
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Wu, Y., Zhang, H., Yang, H. (2009). Research on Parallel HW/SW Partitioning Based on Hybrid PSO Algorithm. In: Hua, A., Chang, SL. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2009. Lecture Notes in Computer Science, vol 5574. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03095-6_43
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DOI: https://doi.org/10.1007/978-3-642-03095-6_43
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