Buffer Analysis for Complete Application Graphs

  • Joachim Keinert
  • Jürgen Teich
Part of the Embedded Systems book series (EMSY)


As already shown in Chapter 6, buffer size determination is an important step in system level design of image processing applications because it helps to improve throughput by avoiding external memories. Furthermore, it is possible to reduce power dissipation and chip sizes and thus costs. Consequently, a buffer analysis technique based on simulation has been presented in the previous chapter that can be applied to arbitrary scheduling strategies. By this means, two different memory mappings, expressed in different memory models, have been compared. Whereas the first one uses a rectangular array structure, the second performs linearization in production order. As a result, it could be shown that both strategies have their advantages and drawbacks and that high-speed applications requiring parallel processing are best covered by the linearized buffer model.


Integer Linear Program Data Element Buffer Size Bilateral Filter Dependency Vector 
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  1. 2.
    Graphviz - graph visualization software.
  2. 3.
  3. 5.
  4. 62.
    Charot, F., Nyamsi, M., Quinton, P., Wagner, C.: Modeling and scheduling parallel data flow systems using structured systems of recurrence equations. In: Proceedings of the 15th IEEE International Conference on Application-Specific Systems, Architectures and Processors (ASAP ’04), pp. 6–16. IEEE Computer Society, Washington, DC (2004)Google Scholar
  5. 72.
    Cormen, T.H., Leiserson, C.E., Rivest, R.L., Stein, C.: Introduction to Algorithms, 2nd edition, chap. Section 24.1: The Bellman-Ford Algorithm, pp. 588–592. MIT Press and McGraw-Hill, Cambridge, MA (2001)MATHGoogle Scholar
  6. 104.
    Feautrier, P.: Parametric integer programming. Oper. Res. 22(3), 243–268 (1988)MathSciNetMATHGoogle Scholar
  7. 105.
    Feautrier, P.: Scalable and structured scheduling. Int. J. Parallel Program. 34(5), 459–487 (2006)CrossRefMathSciNetMATHGoogle Scholar
  8. 143.
    Hu, Q., Vandecappelle, A., Kjeldsberg, P.G., Catthoor, F., Palkovic, M.: Fast memory footprint estimation based on maximal dependency vector calculation. In: Proceedings of the Conference on Design, Automation and Test in Europe (DATE ’07), pp. 379–384. EDA Consortium, San Jose, CA (2007)Google Scholar
  9. 148.
    ILOG: cplex. (2010). Accessed 19 Sep 2010
  10. 152.
    ISO/IEC JTC1/SC29/WG1: JPEG2000 Part I Final Committee Draft Version 1.0 (2002). N1646RGoogle Scholar
  11. 163.
    Keinert, J., Dutta, H., Hannig, F., Haubelt, C., Teich, J.: Model-based synthesis and optimization of static multi-rate image processing algorithms. In: Proceedings of Design, Automation & Test in Europe, pp. 135–140. Nice, France (2009)Google Scholar
  12. 176.
    Kjeldsberg, P., Catthoor, F., Aas, E.J.: Detection of partially simultaneously alive signals in storage requirement estimation for data intensive applications. In: DAC ’01: Proceedings of the 38th conference on Design automation, pp. 365–370. ACM Press, New York, NY (2001)Google Scholar
  13. 177.
    Kjeldsberg, P.G., Catthoor, F., Aas, E.J.: Data dependency size estimation for use in memory optimization. IEEE Trans. CAD Integr. Circuits Syst. 22(7), 908–921 (2003)CrossRefGoogle Scholar
  14. 178.
    Kjeldsberg, P.G., Catthoor, F., Aas, E.J.: Storage requirement estimation for optimized design of data intensive applications. ACM Trans. Des. Autom. Electron. Syst. 9(2), 133–158 (2004)CrossRefGoogle Scholar
  15. 198.
    Li, J.: Image compression: The mathematics of JPEG2000. Modern Signal Process. MSRI Publ. 46, 185–221 (2003)Google Scholar
  16. 271.
    Tarjan, R.: Depth-first search and linear graph algorithms. SIAM J. Comput. 1(2), 146–160 (1972)CrossRefMathSciNetMATHGoogle Scholar
  17. 282.
    Thörnberg, B., Palkovic, M., Hu, Q., Olsson, L., Kjeldsberg, P.G., O’Nils, M., Catthoor, F.: Bit-width constrained memory hierarchy optimization for real-time video systems. IEEE Trans. CAD Integr. Circuits Syst. 26(4), 781–800 (2007)CrossRefGoogle Scholar
  18. 283.
    Tomasi, C., Manduchi, R.: Bilateral filtering for gray and color images. In: ICCV, pp. 839–846. Bombay, India (1998)Google Scholar
  19. 292.
    Verdoolaege, S., Nikolov, H., Stefanov, T.: PN: a tool for improved derivation of process networks. EURASIP J. Embedded Syst. 2007(1), 19–19 (2007)Google Scholar
  20. 302.
    Wiggers, M., Bekooij, M., Smit, G.: Efficient computation of buffer capacities for cyclo-static dataflow graphs. Tech. Rep., Centre for Telematics and Information Technology, University of Twente, Enschede (2006)Google Scholar
  21. 313.
    Zhang, F., Yoo, Y.M., Koh, L.M., Kim, Y.: Nonlinear diffusion in Laplacian pyramid domain for ultrasonic speckle reduction. IEEE Trans. Med. Imaging 26(2), 200–211 (2007)CrossRefGoogle 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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