Parameterized scheduling of topological patterns in signal processing dataflow graphs

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

    Research output: Contribution to journalArticleScientificpeer-review

    1 Citation (Scopus)


    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.

    Original languageEnglish
    Pages (from-to)275-286
    Number of pages12
    JournalJournal of Signal Processing Systems
    Issue number3
    Publication statusPublished - 2013
    Publication typeA1 Journal article-refereed


    • Dataflow
    • Image registration
    • Scheduling
    • Software tools
    • Turbo decoder

    ASJC Scopus subject areas

    • Hardware and Architecture
    • Information Systems
    • Signal Processing
    • Theoretical Computer Science
    • Control and Systems Engineering
    • Modelling and Simulation


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