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Applying Hybrid Neural Fuzzy System to Embedded System Hardware/Software Partitioning

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Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence (ICIC 2007)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4682))

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Abstract

Hardware/Software (HW/SW) partitioning is becoming an increasingly crucial step in embedded system co-design. To cope with roughly assumed parameters in system specification and imprecise benchmarks for judging the solution’s quality, researchers have been trying to find methods for a semi-optimal partitioning scheme in HW/SW partitioning for years. This paper proposes an application of a hybrid neural fuzzy system incorporating Boltzmann machine to the HW/SW partitioning problem. The hybrid neural fuzzy system’s architecture and performance estimation against simulated annealing algorithm are evaluated. The simulation result shows the proposed system outperforms other algorithm in cost and performance.

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References

  1. Wolf, W.: Hardware-software Co-design of Embedded System. In: Proc. of the IEEE, vol. 82, Issue 7, pp. 967–989 (1994)

    Google Scholar 

  2. Staunstrup, J., Wolf, W.: Hardware/Software Co-Design: Principles and Practice. Kluwer Academic Publishers, Dordrecht (1997)

    MATH  Google Scholar 

  3. Barros, E., Rosenstiel, W.: A Method for Hardware Software Partitioning, In: Proc. of CompEuro ’92 Computer Systems and Software Engineering, pp. 580–585 (1992)

    Google Scholar 

  4. Estrin, G.: A Methodology for Design of Digital Systems Supported by SARA at Age One, In: National Computer Conference (1978)

    Google Scholar 

  5. Saha, D., Mitra, R.R., Basu, A.: Hardware Software Partitioning Using Genetic Algorithm, In: Proc. of the Tenth International Conference on VLSI Design, pp. 155–160 (1997)

    Google Scholar 

  6. Arato, P., Juhasz, S., Mann, Z.A., Orban, A., Papp, D.: Hardware-software Partitioning in Embedded System Design. In: Proc. of Intelligent Signal Processing 2003 IEEE International Symposium, pp. 197–202. IEEE Computer Society Press, Los Alamitos (2003)

    Chapter  Google Scholar 

  7. Wu, J., Srikanthan, T.: A Branch-and-Bound Algorithm for Hardware/Software Partitioning. In: Proc. of IEEE Symposium on Signal Processing and Information Technology (ISSPIT), pp. 526–529. IEEE Computer Society Press, Los Alamitos (2004)

    Google Scholar 

  8. Henkel, J., Ernst, R.: An approach to automated hardware/software partitioning using a flexible granularity that is driven by high-level estimation techniques. IEEE Trans. VLSI Syst. 9(2), 273–289 (2001)

    Article  Google Scholar 

  9. Eles, P., Peng, Z., Kuchcinski, K., Doboli, A.: System Level Hardware/Software Partitioning Based on Simulated Annealing and Tabu Search. Journal on Design Automation for Embedded System 2(1), 5–32 (1997)

    Article  Google Scholar 

  10. Ma, T.Y., Li, Z.Q., Yang, J.: A Novel Neural Network Search for Energy-Efficient Hardware-Software Partitioning, In: Machine Learning and Cybernetics, 2006 International Conference, pp. 3053–3058 (2006)

    Google Scholar 

  11. Nauck, D., Klawonn, F., Kruse, R.: Choosing Appropriate Neuro-Fuzzy Models, In: Proc. of EUFIT’94, Aachen, pp. 552–557 (1994)

    Google Scholar 

  12. Lin, C.T., Lee, C.S.G.: A multi-valued Boltzman machine, In: Systems Man and Cybernetics. IEEE Transaction 25(4), 660–669 (1995)

    MathSciNet  Google Scholar 

  13. Ma, H.: Pattern Recognition Using Boltzmann Machine, In: Proc. of Southeastcon ’95 Visualize the Future, 23–29 (1995)

    Google Scholar 

  14. Xiong, Z.H., Chen, J.H., Li, S.K.: Hardware/Software partitioning for platform-based design method, In: Proc. of Asia and South Pacific-Design Automation Conference, vol. 2, 691-696 (2005)

    Google Scholar 

  15. Wang, G., Gong, W.R., Kastner, R.: A New Approach for Task Level Computational Resource Bi-partitionging, In: Proc. of the IASTED Int’l Conf. on Parallel and Distributed Computing and Systems (PDCS), ACTA Press, pp. 434–444 (2003)

    Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science in Kyungwon University, Sujeong-Gu, Seongnam-Si, Gyeonggi-Do, 461-701, Korea

    Yue Huang & YongSoo Kim

Authors
  1. Yue Huang
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  2. YongSoo Kim
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De-Shuang Huang Laurent Heutte Marco Loog

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© 2007 Springer-Verlag Berlin Heidelberg

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Huang, Y., Kim, Y. (2007). Applying Hybrid Neural Fuzzy System to Embedded System Hardware/Software Partitioning. In: Huang, DS., Heutte, L., Loog, M. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence. ICIC 2007. Lecture Notes in Computer Science(), vol 4682. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74205-0_70

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  • DOI: https://doi.org/10.1007/978-3-540-74205-0_70

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-74201-2

  • Online ISBN: 978-3-540-74205-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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