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...

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Vydané v:Proceedings - ACM IEEE Design Automation Conference s. 1 - 6
Hlavní autori: Schaffner, Michael, Gurkaynak, Frank K., Smolic, Aljosa, Kaeslin, Hubert, Benini, Luca
Médium: Konferenčný príspevok..
Jazyk:English
Vydavateľské údaje: IEEE 01.06.2014
Predmet:
Accuracy
Approximate Computing
Cholesky Decomposition
Computational complexity
Equations
Hardware
Hardware Accelerator
Linear systems
Matrix decomposition
Streaming media
Video Processing
ISSN:0738-100X
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Abstract 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.
AbstractList 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.
Author Kaeslin, Hubert
Schaffner, Michael
Gurkaynak, Frank K.
Smolic, Aljosa
Benini, Luca
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  givenname: Frank K.
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  givenname: Aljosa
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  givenname: Hubert
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  givenname: Luca
  surname: Benini
  fullname: Benini, Luca
  email: benini@iis.ee.ethz.ch
  organization: Integrated Syst. Lab., ETH Zurich, Zurich, Switzerland
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Snippet Many video processing algorithms are formulated as least-squares problems that result in large, sparse linear systems. Solving such systems in real time is...
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SubjectTerms Accuracy
Approximate Computing
Cholesky Decomposition
Computational complexity
Equations
Hardware
Hardware Accelerator
Linear systems
Matrix decomposition
Streaming media
Video Processing
Title An approximate computing technique for reducing the complexity of a direct-solver for sparse linear systems in real-time video processing
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