? A Novel Hardware/Software Partitioning Method Based on Position Disturbed Particle Swarm Optimization with Invasive Weed Optimization
Journal of Computer Science and Technology
Quick Search in JCST
 Advanced Search 
      Home | PrePrint | SiteMap | Contact Us | FAQ
 
Indexed by   SCIE, EI ...
Bimonthly    Since 1986
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 Current Issue | Archive | Adv Search << Previous Articles | Next Articles >>
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

Abstract
Reference
Related Articles
Download: [PDF 1749KB]     Export: BibTeX or EndNote (RIS)  
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.
Articles by authors
Xiao-Hu Yan
Fa-Zhi He
Yi-Lin Chen
Keywordshardware/software partitioning   particle swarm optimization   invasive weed optimization   communication cost   parallel computing     
Received 2016-07-27;
Fund:

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.

Corresponding Authors: 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.
Cite this article:   
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
URL:  
http://jcst.ict.ac.cn:8080/jcst/EN/10.1007/s11390-017-1714-2
Copyright 2010 by Journal of Computer Science and Technology