Citation
Thomas Huining Feng, Lynn Wang, wei zheng, Sri Kanajan, Sanjit Seshia. "Automatic Model Generation for Black Box Real-Time Systems". Design, Automation and Test in Europe (DATE) Conference, April, 2007.

Abstract
Embedded systems are often assembled from black box components. System-level analyses, including verification and timing analysis, typically assume the system description, such as RTL or source code, as an input. There is therefore a need to automatically generate formal models of black box components to facilitate analysis. We propose a new method to generate models of real-time embedded systems based on machine learning from execution traces, under a given hypothesis about the system's model of computation. Our technique is based on a novel formulation of the model generation problem as learning a dependency graph that indicates partial ordering between tasks. Tests based on an industry case study demonstrate that the learning algorithm can scale up and that the deduced system model accurately reflects dependencies between tasks in the original design. These dependencies help us formally prove properties of the system and also extract data dependencies that are not explicitly stated in the specifications of black box components.

Electronic downloads

• http://www.eecs.berkeley.edu/~tfeng/papers/fwzks06date.pdf
• fwzks06date.pdf · application/pdf · 188 kbytes

• HTML

  Thomas Huining Feng, Lynn Wang, wei zheng, Sri Kanajan,
  Sanjit Seshia. <a
  href="http://chess.eecs.berkeley.edu/pubs/244.html"
  >Automatic Model Generation for Black Box Real-Time
  Systems</a>, Design, Automation and Test in Europe
  (DATE) Conference, April, 2007.

• Plain text

  Thomas Huining Feng, Lynn Wang, wei zheng, Sri Kanajan,
  Sanjit Seshia. "Automatic Model Generation for Black
  Box Real-Time Systems". Design, Automation and Test in
  Europe (DATE) Conference, April, 2007.

• BibTeX

  @inproceedings{FengWangzhengKanajanSeshia07_AutomaticModelGenerationForBlackBoxRealTimeSystems,
      author = {Thomas Huining Feng and Lynn Wang and wei zheng
                and Sri Kanajan and Sanjit Seshia},
      title = {Automatic Model Generation for Black Box Real-Time
                Systems},
      booktitle = {Design, Automation and Test in Europe (DATE)
                Conference},
      month = {April},
      year = {2007},
      abstract = { Embedded systems are often assembled from black
                box components. System-level analyses, including
                verification and timing analysis, typically assume
                the system description, such as RTL or source
                code, as an input. There is therefore a need to
                automatically generate formal models of black box
                components to facilitate analysis. We propose a
                new method to generate models of real-time
                embedded systems based on machine learning from
                execution traces, under a given hypothesis about
                the system's model of computation. Our technique
                is based on a novel formulation of the model
                generation problem as learning a dependency graph
                that indicates partial ordering between tasks.
                Tests based on an industry case study demonstrate
                that the learning algorithm can scale up and that
                the deduced system model accurately reflects
                dependencies between tasks in the original design.
                These dependencies help us formally prove
                properties of the system and also extract data
                dependencies that are not explicitly stated in the
                specifications of black box components.},
      URL = {http://chess.eecs.berkeley.edu/pubs/244.html}
  }
  

Posted by Thomas Huining Feng on 14 May 2007.
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