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Multi-objective Q-bit Coding Genetic Algorithm for Hardware-Software Co-synthesis of Embedded Systems

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Simulated Evolution and Learning (SEAL 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4247))

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Abstract

One of the key tasks in Hardware-Software Co-design is to optimally allocate, assign, and schedule resources to achieve a good balance among performance, cost, power consumption, etc. So it’s a typical multi-objective optimization problem. In this paper, a Multi-objective Q-bit coding genetic algorithm (MoQGA) is proposed to solve HW-SW co-synthesis problem in HW-SW co-design of embedded systems. The algorithm utilizes the Q-bit probability representation to model the promising area of solution space, uses multiple Q-bit models to perform search in a parallel manner, uses modified Q-bit updating strategy and quantum crossover operator to implement the efficient global search, uses an archive to preserve and select pareto optima, uses Timed Task Graph to describe the system functions, introduces multi-PRI scheduling strategy and PE slot-filling strategy to improve the time performance of system. Experimental results show that the proposed algorithm can solve the multi-objective co-synthesis problem effectively and efficiently.

Supported by the National Natural Science Foundation of China (No.60401015, No. 60572012), and the Nature Science Foundation of Anhui province (No. 050420201).

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Author information

Authors and Affiliations

  1. Nature Inspired Computation and Applications Laboratory, University of Science and Technology of China, Hefei, 230026, China

    Wei Wen-long, Li Bin, Zou Yi & Zhuang Zhen-quan

Authors
  1. Wei Wen-long
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  2. Li Bin
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  3. Zou Yi
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  4. Zhuang Zhen-quan
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Editor information

Editors and Affiliations

  1. Department of Industrial Engineering and Management, Cheng Shiu University, Kaohsiung County Taiwan, ROC

    Tzai-Der Wang

  2. School of Computer Science and information Technology, RMIT University, VIC 3001, Melbourne, Australia

    Xiaodong Li

  3. AI-ECON Research Center, Department of Economics, National Chengchi University, 11623, Taipei, Taiwan

    Shu-Heng Chen

  4. Department of Computer Science and Technology, University of Sci. & Tech. of China, 230026, Hefei, Anhui, P.R. China

    Xufa Wang

  5. School of Information Technology and Electrical Engineering, University of New South Wales, Australian Defence Force Academy, Canberra, Australia

    Hussein Abbass

  6. The Univertity of Tokyo, Japan

    Hitoshi Iba

  7. Department of Computer, University of Science and Technology of China, 230027, Hefei, China

    Guo-Liang Chen

  8. University of Birmingham, Birmingham, UK

    Xin Yao

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© 2006 Springer-Verlag Berlin Heidelberg

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Wen-long, W., Bin, L., Yi, Z., Zhen-quan, Z. (2006). Multi-objective Q-bit Coding Genetic Algorithm for Hardware-Software Co-synthesis of Embedded Systems. In: Wang, TD., et al. Simulated Evolution and Learning. SEAL 2006. Lecture Notes in Computer Science, vol 4247. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11903697_109

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  • DOI: https://doi.org/10.1007/11903697_109

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-47331-2

  • Online ISBN: 978-3-540-47332-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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