Windowed Data Flow (WDF)

Part of the Embedded Systems book series (EMSY)


Modern image processing applications not only induce huge computational load but also are characterized by increasing complexity. As exemplarily shown in Section 2.2, they typically consist of a mixture of static and data-dependent algorithms and operate on both one-dimensional and multidimensional streams of data. Efficient implementation is only possible by exploiting different kinds of parallelism, namely task, data, and operation-level parallelism (see Section 2.5). Out-of-order communication and sliding windows with parallel data access require complex communication synthesis.


Seed Image Read Operation Data Flow Graph System Level Design Virtual Token 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 50.
    Buck, J.T.: Scheduling dynamic dataflow graphs with bounded memory using the token flow model. Ph.D. thesis, University of California at Berkeley (1993)Google Scholar
  2. 152.
    ISO/IEC JTC1/SC29/WG1: JPEG2000 Part I Final Committee Draft Version 1.0 (2002). N1646RGoogle Scholar
  3. 160.
    Karp, R.M., Miller, R.E.: Properties of a model for parallel computations: Determinacy, termination and queuing. SIAM J. Appl. Math. 14(6), 1390–1411 (1966)CrossRefMathSciNetMATHGoogle Scholar
  4. 165.
    Keinert, J., Haubelt, C., Teich, J.: Windowed Synchronous Data Flow (WSDF). Tech. Rep. 02-2005, University of Erlangen-Nuremberg, Institut for Hardware-Software-Co-Design (2005)Google Scholar
  5. 166.
    Keinert, J., Haubelt, C., Teich, J.: Modeling and analysis of windowed synchronous algorithms. ICASSP2006 III, 892–895 (2006)Google Scholar
  6. 193.
    Lee, E.A., Messerschmitt, D.G.: Static scheduling of synchronous data flow programs for digital signal processing. IEEE Trans. Comput. C-36(1), 24–35 (1987)CrossRefGoogle Scholar
  7. 215.
    Murthy, P.K., Lee, E.A.: Multidimensional synchronous dataflow. IEEE Trans. Signal Process. 50(7), 2064–2079 (2002)CrossRefGoogle Scholar
  8. 247.
    Reiter, R.: Scheduling parallel computations. J. ACM 15(4), 590–599 (1968)CrossRefMATHGoogle Scholar
  9. 248.
    Richardson, I.E.G.: H.264 and MPEG-4 Video Compression – Video Coding for Next-generation Multimedia. Wiley, West Sussex, England (2003)CrossRefGoogle Scholar
  10. 294.
    Vincent, L.: Morphological grayscale reconstruction in image analysis: applications and efficient algorithms. IEEE Trans. Image Process. 2(2), 176–201 (1993)CrossRefGoogle Scholar
  11. 312.
    Zebelein, C., Falk, J., Haubelt, C., Teich, J.: Classification of general data flow actors into known models of computation. In: Proc. 6th ACM/IEEE International Conference on Formal Methods and Models for Codesign (MEMOCODE 2008), pp. 119–128. Anaheim, CA (2008)Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  1. 1.NürnbergGermany
  2. 2.Department of Computer Science 12University of Erlangen-NurembergErlangenGermany

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