An approximate computing technique for reducing the complexity of a direct-solver for sparse linear systems in real-time video processing

Many video processing algorithms are formulated as least-squares problems that result in large, sparse linear systems. Solving such systems in real time is very demanding. This paper focuses on reducing the computational complexity of a direct Cholesky-decomposition-based solver. Our approximation s...

Full description

Saved in:
Bibliographic Details
Published in:Proceedings - ACM IEEE Design Automation Conference pp. 1 - 6
Main Authors: Schaffner, Michael, Gurkaynak, Frank K., Smolic, Aljosa, Kaeslin, Hubert, Benini, Luca
Format: Conference Proceeding
Language:English
Published: IEEE 01.06.2014
Subjects:
Accuracy
Approximate Computing
Cholesky Decomposition
Computational complexity
Equations
Hardware
Hardware Accelerator
Linear systems
Matrix decomposition
Streaming media
Video Processing
ISSN:0738-100X
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Many video processing algorithms are formulated as least-squares problems that result in large, sparse linear systems. Solving such systems in real time is very demanding. This paper focuses on reducing the computational complexity of a direct Cholesky-decomposition-based solver. Our approximation scheme builds on the observation that, in well-conditioned problems, many elements in the decomposition nearly vanish. Such elements may be pruned from the dependency graph with mild accuracy degradation. Using an example from image-domain warping, we show that pruning reduces the amount of operations per solve by over 75 %, resulting in significant savings in computing time, area or energy.
ISSN:0738-100X
DOI:10.1145/2593069.2593082