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Towards Performance Prediction of Compositional Models in Industrial GALS Designs

  • Nicolas Coste
  • Holger Hermanns
  • Etienne Lantreibecq
  • Wendelin Serwe
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5643)

Abstract

Systems and Networks on Chips (NoCs) are a prime design focus of many hardware manufacturers. In addition to functional verification, which is a difficult necessity, the chip designers are facing extremely demanding performance prediction challenges, such as the need to estimate the latency of memory accesses over the NoC. This paper attacks this problem in the setting of designing globally asynchronous, locally synchronous systems (GALS). We describe foundations and applications of a combination of compositional modeling, model checking, and Markov process theory, to arrive at a viable approach to compute performance quantities directly on industrial, functionally verified GALS models.

Keywords

Performance Prediction Markov Decision Process Operational Semantic Steady State Probability Virtual Channel 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Nicolas Coste
    • 1
    • 2
  • Holger Hermanns
    • 1
    • 3
  • Etienne Lantreibecq
    • 2
  • Wendelin Serwe
    • 1
  1. 1.INRIA Grenoble – Rhône-Alpes, VASY project teamFrance
  2. 2.STMicroelectronics GrenobleFrance
  3. 3.Universität des SaarlandesGermany

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