Applying Out-of-Core QR Decomposition Algorithms on FPGA-Based Systems

QR decomposition, especially through the means of Householder transformation, is often used to solve least squares problems. A matrix to be decomposed with this method is usually very large, often large enough that it is not able to fit into the main memory of a workstation, let alone the internal m...

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Bibliographic Details
Main Authors: Tai, Yi-Gang, Lo, Chia-Tien Dan, Psarris, Kleanthis
Format: Conference Proceeding
Language:English
Subjects:
Casting
Computer science
Equations
Field programmable gate arrays
Iterative algorithms
Least squares methods
Matrix decomposition
Memory architecture
Software libraries
Workstations
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Summary:QR decomposition, especially through the means of Householder transformation, is often used to solve least squares problems. A matrix to be decomposed with this method is usually very large, often large enough that it is not able to fit into the main memory of a workstation, let alone the internal memory of an FPGA nowadays. Efficient out-of-core algorithms have been developed to address the factorization of large matrices. This paper describes the application of variants of Householder QR decomposition on FPGA-based systems. More specifically, issues on applying out-of-core algorithms to the relatively small internal memory architecture of FPGA's are investigated.
ISSN:1946-147X
1946-1488
DOI:10.1109/FPL.2007.4380630