Hardware/software partitioning algorithm based on the combination of genetic algorithm and tabu search

To solve the hardware/software (HW/SW) partitioning problem of a single Central Processing Unit (CPU) system, a hybrid algorithm of Genetic Algorithm (GA) and Tabu Search(TS) is studied. Firstly, the concept hardware orientation is proposed and then used in creating the initial colony of GA and the mutation, which reduces the randomicity of initial colony and the blindness of search. Secondly, GA is run, the crossover and mutation probability become smaller in the process of GA, thus they not only ensure a big search space in the early stages, but also save the good solution for later browsing. Finally, the result of GA is used as initial solution of TS, and tabu length adaptive method is put forward in the process of TS, which can improve the convergence speed. From experimental statistics, the efficiency of proposed algorithm outperforms comparison algorithm by up to 25% in a large-scale problem, what is more, it can obtain a better solution. In conclusion, under specific conditions, the proposed algorithm has higher efficiency and can get better solutions

Hardware/software partitioning of streaming applications for multi-processor system-on-chip

Hardware/software (HW/SW) co-design has emerged as a crucial and integral part in the development of various embedded applications. Moreover, the increases in the number of embedded multimedia and medical applications make streaming throughput an important attribute of Multi-Processor System-on-Chip (MPSoC). As an important development step, HW/SW partitioning affects the system performance. This paper formulates the optimization of HW/SW partitioning aiming at maximizing streaming throughput with predefined area constraint, targeted for multi-processor system with hardware accelerator sharing capability. Software-oriented and hardware-oriented greedy heuristics for HW/SW partitioning are proposed, as well as a branch-and-bound algorithm with best-first search that utilizes greedy results as initial best solution. Several random graphs and two multimedia applications (JPEG encoder and MP3 decoder) are used for performance benchmarking against brute force ground truth. Results show that the proposed greedy algorithms produce fast solutions which achieve 87.7% and 84.2% near-optimal solution respectively compared to ground truth result. With the aid of greedy result as initial solution, the proposed branch-and-bound algorithm is able to produce ground truth solution up to 2.4741e+8 times faster in HW/SW partitioning time compared to exhaustive brute force method

efficient heuristic and tabu search for hardware/software partitioning

软硬件划分是软硬件协同设计的关键环节,它决定系统中哪些组件由软件实现,哪些由硬件实现。软硬件划分问题已被证明是NP完全问题。将一类软硬件划分问题看作变异的0-1背包问题,在求解背包问题的算法基础上构造出软硬件划分问题的优质启发解。此外,采用禁忌搜索(Tabu Search)算法对求得的启发解进行改进,在软件开销和通信开销满足一定约束的条件下,使得硬件开销尽可能小。实验结果证明,所提算法对当前最新算法的改进最大可达到28%