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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.