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Pacific Rim International Conference on Artificial Intelligence

PRICAI 1996: PRICAI'96: Topics in Artificial Intelligence pp 276–287Cite as

High-level synthesis optimisation with genetic algorithms

  • Constraint Satisfaction and Optimization
  • Conference paper
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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1114))

Abstract

The results of a genetic algorithm optimisation of the scheduling and allocation phases of high-level synthesis are reported. Scheduling and allocation are NP complete, multi-objective phases of high-level synthesis. A high-level synthesis system must combine the two problems to produce optimal results. The genetic algorithm described provides a robust and efficient method of search capable of combining scheduling and allocation phases, and responding to the multiple and changing objectives of high-level synthesis. The results show the genetic algorithm succeeds in finding optimal or near optimal results to classic benchmarks in small computational time spans.

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References

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Author information

Authors and Affiliations

  1. Department of Computer Science, University of Adelaide, 5005, Adelaide, South Australia

    Jem Daalder & Peter W. Eklund

  2. Department of Industrial and Systems Engineering, Hosei University, Koganei-shi, Tokyo

    Kenji Ohmori

Authors
  1. Jem Daalder
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  2. Peter W. Eklund
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  3. Kenji Ohmori
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Editor information

Norman Foo Randy Goebel

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© 1996 Springer-Verlag Berlin Heidelberg

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Daalder, J., Eklund, P.W., Ohmori, K. (1996). High-level synthesis optimisation with genetic algorithms. In: Foo, N., Goebel, R. (eds) PRICAI'96: Topics in Artificial Intelligence. PRICAI 1996. Lecture Notes in Computer Science, vol 1114. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-61532-6_24

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  • DOI: https://doi.org/10.1007/3-540-61532-6_24

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-61532-3

  • Online ISBN: 978-3-540-68729-0

  • eBook Packages: Springer Book Archive

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