Journal of Signal Processing Systems

, Volume 71, Issue 3, pp 275–286 | Cite as

Parameterized Scheduling of Topological Patterns in Signal Processing Dataflow Graphs

  • Lai-Huei Wang
  • Chung-Ching Shen
  • Shenpei Wu
  • Shuvra S. Bhattacharyya


In recent work, a graphical modeling construct called “topological patterns” has been shown to enable concise representation and direct analysis of repetitive dataflow graph sub-structures in the context of design methods and tools for digital signal processing systems (Sane et al. 2010). In this paper, we present a formal design method for specifying topological patterns and deriving parameterized schedules from such patterns based on a novel schedule model called the scalable schedule tree. The approach represents an important class of parameterized schedule structures in a form that is intuitive for representation and efficient for code generation. Through application case studies involving image processing and wireless communications, we demonstrate our methods for topological pattern representation, scalable schedule tree derivation, and associated dataflow graph code generation.


Scheduling Dataflow Software tools Image registration Turbo decoder 


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Copyright information

© Springer Science+Business Media New York 2012

Authors and Affiliations

  • Lai-Huei Wang
    • 1
  • Chung-Ching Shen
    • 1
  • Shenpei Wu
    • 1
  • Shuvra S. Bhattacharyya
    • 1
  1. 1.Department of Electrical and Computer Engineering,and Institute for Advanced Computer StudiesUniversity of MarylandCollege ParkUSA

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