? 基于野草扰动粒子群算法的新型软硬件划分方法
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Journal of Computer Science and Technology 2017, Vol. 32 Issue (2) :340-355    DOI: 10.1007/s11390-017-1714-2
Artificial Intelligence and Pattern Recognition << Previous Articles | Next Articles >>
基于野草扰动粒子群算法的新型软硬件划分方法
Xiao-Hu Yan, Member, CCF, Fa-Zhi He*, Member, CCF, Yi-Lin Chen
1 State Key Laboratory of Software Engineering, Wuhan University, Wuhan 430072, China;
2 School of Computer Science, Wuhan University, Wuhan 430072, China
A Novel Hardware/Software Partitioning Method Based on Position Disturbed Particle Swarm Optimization with Invasive Weed Optimization
Xiao-Hu Yan, Member, CCF, Fa-Zhi He*, Member, CCF, Yi-Lin Chen
1 State Key Laboratory of Software Engineering, Wuhan University, Wuhan 430072, China;
2 School of Computer Science, Wuhan University, Wuhan 430072, China

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摘要 随着嵌入式系统设计复杂度的增加,软硬件划分已成为软硬件协同设计中的关键优化难题。文中提出基于野草扰动粒子群算法(Position Disturbed Particle Swarm Optimization with Invasive Weed Optimization,PDPSO-IWO)的新型软硬件划分方法。当地松鼠察觉到有潜在捕食者的时候,就会发出警告信息,通知同类远离危险。在PDPSO中,通过模拟这种智能行为,粒子远离群体中全局最差的个体,保持种群多样性,以减少陷入局部最优的可能性。通过改进野草算法(Invasive Weed Optimization,IWO)的初始化和繁殖策略,将IWO集成到PDPSO-IWO中,以提高算法在当前全局最优解附近的搜索精度。将PDPSO和改进的IWO融合为PDPSO-IWO算法,能同时增强搜索的多样性和集中性。提出HNodeRank方法用于初始化PDPSO-IWO的种群,能进一步提升解的质量。由于软硬划分算法中最耗时的过程是计算软硬件的通信代价,文中采用GPU加速该过程,从而能有效减少求解大规模软硬件划分问题的运行时间。最后,通过基准任务和特大规模任务测试集验证了本文方法的有效性。
关键词软硬件划分   粒子群算法   野草算法   通信代价   并行计算     
Abstract: With the development of the design complexity in embedded systems, hardware/software (HW/SW) partitioning becomes a challenging optimization problem in HW/SW co-design. A novel HW/SW partitioning method based on position disturbed particle swarm optimization with invasive weed optimization (PDPSO-IWO) is presented in this paper. It is found by biologists that the ground squirrels produce alarm calls which warn their peers to move away when there is potential predatory threat. Here, we present PDPSO algorithm, in each iteration of which the squirrel behavior of escaping from the global worst particle can be simulated to increase population diversity and avoid local optimum. We also present new initialization and reproduction strategies to improve IWO algorithm for searching a better position, with which the global best position can be updated. Then the search accuracy and the solution quality can be enhanced. PDPSO and improved IWO are synthesized into one single PDPSO-IWO algorithm, which can keep both searching diversification and searching intensification. Furthermore, a hybrid NodeRank (HNodeRank) algorithm is proposed to initialize the population of PDPSO-IWO, and the solution quality can be enhanced further. Since the HW/SW communication cost computing is the most time-consuming process for HW/SW partitioning algorithm, we adopt the GPU parallel technique to accelerate the computing. In this way, the runtime of PDPSO-IWO for large-scale HW/SW partitioning problem can be reduced efficiently. Finally, multiple experiments on benchmarks from state-of-the-art publications and large-scale HW/SW partitioning demonstrate that the proposed algorithm can achieve higher performance than other algorithms.
Keywordshardware/software partitioning   particle swarm optimization   invasive weed optimization   communication cost   parallel computing     
Received 2016-07-27;
本文基金:

The work was supported by the National Natural Science Foundation of China under Grant No. 61472289 and the National Key Research and Development Project of China under Grant No. 2016YFC0106305.

通讯作者: Fa-Zhi He     Email: fzhe@whu.edu.cn
About author: Xiao-Hu Yan received his B.S. degree in information and computing science from Huazhong Agricultural University, Wuhan, in 2008, and his M.S. degree in computer application from North China Electric Power University, Beijing, in 2010. Currently, he is a Ph.D. candidate of the School of Computer Science in Wuhan University, Wuhan. His research interests include hardware/software partitioning and optimization algorithm.
引用本文:   
Xiao-Hu Yan, Fa-Zhi He, Yi-Lin Chen.基于野草扰动粒子群算法的新型软硬件划分方法[J]  Journal of Computer Science and Technology , 2017,V32(2): 340-355
Xiao-Hu Yan, Fa-Zhi He, Yi-Lin Chen.A Novel Hardware/Software Partitioning Method Based on Position Disturbed Particle Swarm Optimization with Invasive Weed Optimization[J]  Journal of Computer Science and Technology, 2017,V32(2): 340-355
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