Parallel singular value decomposition of complex matrices usingmultidimensional CORDIC algorithms

Authors:
Shen-Fu Hsiao;J.-M. Delosme
Affiliations:
Inst. of Comput. Eng., Nat. Sun Yat-Sen Univ., Kaohsiung;-
Venue:
IEEE Transactions on Signal Processing
Year:
1996

Citing 0
Cited 4

Design, Implementation and Analysis of a New Redundant CORDIC Processor with Constant Scaling Factor and Regular Structure

Journal of VLSI Signal Processing Systems
Redundant Constant-Factor Implementation of Multi-Dimensional CORDIC andIts Application to Complex SVD

Journal of VLSI Signal Processing Systems - special issue on CORDIC
Progressive image transmission using singular value decomposition

EGMM'04 Proceedings of the Seventh Eurographics conference on Multimedia
Instruction Set Extensions for Matrix Decompositions on Software Defined Radio Architectures

Journal of Signal Processing Systems

Quantified Score

Hi-index	35.68

Visualization

Abstract

The singular value decomposition (SVD) of complex matrices is computed in a highly parallel fashion on a square array of processors using Kogbetliantz's analog of Jacobi's eigenvalue decomposition method. To gain further speed, new algorithms for the basic SVD operations are proposed and their implementation as specialized processors is presented. The algorithms are 3-D and 4-D extensions of the CORDIC algorithm for plane rotations. When these extensions are used in concert with an additive decomposition of 2×2 complex matrices, which enhances parallelism, and with low resolution rotations early on in the SVD process, which reduce operation count, a fivefold speedup can be achieved over the fastest alternative approach