Memory size computation for multimedia processing applications

In real-time multimedia processing systems a very large part of the power consumption is due to the data storage and data transfer. Moreover, the area cost is often largely dominated by the memory modules. The computation of the memory size is an important step in the process of designing an optimized (for area and/or power) memory architecture for multimedia processing systems. This paper presents a novel non-scalar approach for computing exactly the memory size in real-time multimedia algorithms. This methodology uses both algebraic techniques specific to the data-flow analysis used in modern compilers, and also recent advances in the theory of integral polyhedra. In contrast with all the previous works which are only estimation methods, this approach performs exact memory computations even for applications with a large number of scalar signals

[1]  Sharad Malik,et al.  Exact memory size estimation for array computations , 2000, IEEE Trans. Very Large Scale Integr. Syst..

[2]  Fadi J. Kurdahi,et al.  REAL: A Program for REgister ALlocation , 1987, 24th ACM/IEEE Design Automation Conference.

[3]  George B. Dantzig,et al.  Fourier-Motzkin Elimination and Its Dual , 1973, J. Comb. Theory A.

[4]  Steven S. Muchnick,et al.  Advanced Compiler Design and Implementation , 1997 .

[5]  Francky Catthoor,et al.  Custom Memory Management Methodology: Exploration of Memory Organisation for Embedded Multimedia System Design , 1998 .

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

[7]  Jan M. Rabaey,et al.  Memory Estimation for High Level Synthesis , 1994, 31st Design Automation Conference.

[8]  D. Avis A Revised Implementation of the Reverse Search Vertex Enumeration Algorithm , 2000 .

[9]  William Pugh,et al.  A practical algorithm for exact array dependence analysis , 1992, CACM.

[10]  Alexander I. Barvinok,et al.  A Polynomial Time Algorithm for Counting Integral Points in Polyhedra when the Dimension Is Fixed , 1993, FOCS.

[11]  Lothar Thiele,et al.  Compiler Techniques for Massive Parallel Architectures , 1992 .

[12]  D. Avis lrs : A Revised Implementation of the Rev rse Search Vertex Enumeration Algorithm , 1998 .

[13]  Francky Catthoor,et al.  Custom Memory Management Methodology , 1998, Springer US.

[14]  Hugo De Man,et al.  Background memory area estimation for multidimensional signal processing systems , 1995, IEEE Trans. Very Large Scale Integr. Syst..

[15]  Leon Stok,et al.  Foreground memory management in data path synthesis , 1992, Int. J. Circuit Theory Appl..

[16]  Alexander Schrijver,et al.  Theory of linear and integer programming , 1986, Wiley-Interscience series in discrete mathematics and optimization.

[17]  Erik Brockmeyer,et al.  Data and memory optimization techniques for embedded systems , 2001, TODE.

[18]  Jesús A. De Loera,et al.  Effective lattice point counting in rational convex polytopes , 2004, J. Symb. Comput..

[19]  Keshab K. Parhi Calculation of minimum number of registers in arbitrary life time chart , 1994, Proceedings of 7th International Conference on VLSI Design.