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Singular value decomposition (SVD) has become a standard linear algebra tool in modern digital signal processing. CORDIC based SVD algorithms are among the most popular SVD algorithms which exhibit good numerical properties. The speed of the sequential algorithms is however limited by the recursive feedback loops in the underlying signal flow graph. The critical loop computation time is proportional to the size of the problem which prohibits pipelined processing. This paper addresses the derivation of parallel architectures for SVD updating algorithms. An algorithm transformation approach based on re-timing and matrix associativity is presented to drive parallel SVD updating architectures. These architectures have critical loop computation time independent of the problem size and are suitable for CORDIC arithmetic based VLSI implementations.