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Systolic Array Computation Of The Singular Value Decomposition

Alan M. Finn, Franklin T. Luk, Christopher Pottle

Open publisher page 16 citations

Abstract

Linear time computation of the singular value decomposition (SVD) would be useful in many real time signal processing applications. Two algorithms for the SVD have been developed for implementation on a quadratic array of processors. A specific architecture is proposed and we demonstrate the mapping of the algorithms to the architecture. The algorithms and architecture together have been verified by functional level and register transfer level simulation.

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What this paper is about

Linear time computation of the singular value decomposition (SVD) would be useful in many real time signal processing applications. Two algorithms for the SVD have been developed for implementation on a quadratic array of processors. A specific architecture is proposed and we demonstrate the mapping of the algorithms to the architecture. The algorithms and architecture together have been verified by functional level and register transfer level simulation.

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OpenAlex reports 16 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

Linear time computation of the singular value decomposition (SVD) would be useful in many real time signal processing applications. Two algorithms for the SVD have been developed for implementation on a quadratic array of processors. A specific architecture is proposed and we demonstrate the mapping of the algorithms to the architecture. The algorithms and architecture together have been verified by functional level and register transfer level simulation.

Key concepts: Singular value decomposition, Computation, Systolic array, Parallel computing, Computer science, Quadratic equation, Decomposition, Architecture

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