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Parameterized scheduling of topological patterns in signal processing dataflow graphs

Research output: Contribution to journalArticleScientificpeer-review

Details

Original languageEnglish
Pages (from-to)275-286
Number of pages12
JournalJournal of Signal Processing Systems
Volume71
Issue number3
DOIs
Publication statusPublished - 2013
Publication typeA1 Journal article-refereed

Abstract

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.

Keywords

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