Buffer Analysis for Complete Application Graphs

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.

[1]  Fan Zhang,et al.  Nonlinear Diffusion in Laplacian Pyramid Domain for Ultrasonic Speckle Reduction , 2007, IEEE Transactions on Medical Imaging.

[2]  Francky Catthoor,et al.  Detection of partially simultaneously alive signals in storage requirement estimation for data intensive applications , 2001, DAC '01.

[3]  Francky Catthoor,et al.  Data dependency size estimation for use in memory optimization , 2003, IEEE Trans. Comput. Aided Des. Integr. Circuits Syst..

[4]  Clifford Stein,et al.  Introduction to Algorithms, 2nd edition. , 2001 .

[5]  Christian Haubelt,et al.  Model-based synthesis and optimization of static multi-rate image processing algorithms , 2009, 2009 Design, Automation & Test in Europe Conference & Exhibition.

[6]  François Charot,et al.  Modeling and scheduling parallel data flow systems using structured systems of recurrence equations , 2004 .

[7]  Todor Stefanov,et al.  pn: A Tool for Improved Derivation of Process Networks , 2007, EURASIP J. Embed. Syst..

[8]  Ronald L. Rivest,et al.  Introduction to Algorithms , 1990 .

[9]  P. Feautrier Parametric integer programming , 1988 .

[10]  Jin Li Image Compression: the Mathematics of JPEG 2000 , 2002 .

[11]  Paul Feautrier,et al.  Scalable and Structured Scheduling , 2006, International Journal of Parallel Programming.

[12]  Gerard J. M. Smit,et al.  Efficient Computation of Buffer Capacities for Cyclo-Static Dataflow Graphs , 2007, 2007 44th ACM/IEEE Design Automation Conference.

[13]  Francky Catthoor,et al.  Fast Memory Footprint Estimation based on Maximal Dependency Vector Calculation , 2007, 2007 Design, Automation & Test in Europe Conference & Exhibition.

[14]  Francky Catthoor,et al.  Storage requirement estimation for optimized design of data intensive applications , 2004, TODE.

[15]  Robert E. Tarjan,et al.  Depth-First Search and Linear Graph Algorithms , 1972, SIAM J. Comput..

[16]  Francky Catthoor,et al.  Bit-Width Constrained Memory Hierarchy Optimization for Real-Time Video Systems , 2007, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems.

[17]  Roberto Manduchi,et al.  Bilateral filtering for gray and color images , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).