Scalable Representation of Dataflow Graph Structures Using Topological Patterns

Abstract

Tools for designing signal processing systems with their semantic foundation in dataflow modeling often use high-level graphical user interface (GUI) or text based languages that allow specifying applications as directed graphs. Such graphical representations serve as an initial reference point for further analysis and optimizations that lead to platform-specific implementations. For large-scale applications the underlying graphs often consist of smaller substructures that repeat multiple times. To enable more concise representation and direct analysis of such substructures in the context of high level DSP specification languages and design tools, we develop the modeling concept of topological patterns, and propose ways for supporting this concept in a high-level language. We augment the DIF language -- "a language for specifying DSP-oriented dataflow graphs" -- with constructs for supporting topological patterns, and we show how topological patterns can be effective in various aspects of embedded signal processing design flows using specific application examples.

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Document Details

Document Type
Technical Report
Publication Date
Oct 01, 2010
Accession Number
ADA528944

Entities

People

  • Gunasekaran Seetharaman
  • Hojin Kee
  • Nimish Sane
  • Shuvra S. Bhattacharyya

Organizations

  • Air Force Research Laboratory

Tags

Communities of Interest

  • Energy and Power Technologies
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Air Force Research Laboratories
  • Algorithms
  • Coding
  • Computations
  • Computer Programming
  • Computer Programs
  • Computers
  • Field Programmable Gate Arrays
  • Graphical User Interface
  • Indexes
  • Language
  • Models
  • Programming Languages
  • Signal Processing
  • Specifications
  • Topology
  • Two Dimensional

Fields of Study

  • Computer science
  • Engineering

Readers

  • Computational Linguistics
  • Database Systems and Applications
  • Graph Algorithms and Convex Optimization.