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An evolutionary approach to hardware/software partitioning

  • Xiaobo (Sharon) Hu
  • Garrison Greenwood
  • Joseph G. D'Ambrosio
Applications of Evolutionary Computation Evolutionary Computation in Computer Science and Operations Research
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1141)

Abstract

In this paper, we present an approach to hardware/software codesign of real-time embedded systems. Two of the difficulties associated with codesign are handling tradeoffs among multiple attributes and exploring a large design space. We use a combination of techniques from the evolutionary computation and utility theory fields to address these problem areas. A real-time microcontroller-based design example is presented to illustrate our approach.

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References

  1. 1.
    Special editions on hardware/software codesign appearing in IEEE Design & Test of Computers, vol.10, no.3 & no. 4, 1993Google Scholar
  2. 2.
    J.G. D'Ambrosio and X. Hu, “Configuration-level hardware/software partition for real-time embedded systems,” Proceedings of the Third International Workshop on Hardware-Software Co-Design, 34–41, 1994Google Scholar
  3. 3.
    C. Fonseca and P. Fleming, “An Overview of Evolutionary Algorithms in Multiobjective Optimization”, Evolutionary Computation, Vol. 3, No. 1, 1–17, 1995Google Scholar
  4. 4.
    G. Greenwood, X. Hu, and J. D'Ambrosio, “Fitness Functions for Multipleobjective Optimization Problems: Combining Preferences With Pareto Rankings”, FOGA4 (to appear)Google Scholar
  5. 5.
    C. White, A. Sage, and S. Dozono, “A Model of Multiattribute Decisionmaking and Tradeoff Weight Determination Under Uncertainty”, IEEE Trans. Syst., Man, Cybern.”, Vol SMC-14, 223–229, 1984Google Scholar
  6. 6.
    R.L. Keeney and H. Raiffa, Decisions with Multiple Objectives: Preferences and Value Tradeoffs, John Wiley & Sons, NY, 1976Google Scholar
  7. 7.
    J. Horn and N. Nafpliotis, “ Multiobjective Optimization using the Niched Pareto Genetic Algorithm”, IlliGAL Report 93005, University of Illinois at Urbana-ChampaignGoogle Scholar
  8. 8.
    D. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley Pub. Co., 1989Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1996

Authors and Affiliations

  • Xiaobo (Sharon) Hu
    • 1
  • Garrison Greenwood
    • 1
  • Joseph G. D'Ambrosio
    • 2
  1. 1.Western Michigan UniversityKalamazooUSA
  2. 2.General Motors R&D CenterWarrenUSA

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