The cordicalgorithm is a well-known iterative method for the computation of vector rotation. However, the major disadvantage is its relatively slow computational speed. For applications that require forward rotation ...
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The cordicalgorithm is a well-known iterative method for the computation of vector rotation. However, the major disadvantage is its relatively slow computational speed. For applications that require forward rotation (or vector rotation) only, we propose a new scheme, the modified vector rotational cordic (mvr-cordic) algorithm, to improve the speed performance of cordicalgorithm. The basic idea of the proposed scheme is to reduce the iteration number directly while maintaining the SQNR performance. This can be achieved by modifying the basic microrotation procedure of cordicalgorithm. Meanwhile, three searching algorithms are suggested to find the corresponding directional and rotational sequences so as to obtain the best SQNR performance. Three SQNR performance refinement schemes are also suggested in this paper. Namely, the selective prerotation scheme, selective scaling scheme, and iteration-tradeoff scheme. They can reduce and balance the quantization errors encountered in both microrotation and scaling phases so as to further improve the overall SQNR performance. Then, by combining these three refinement schemes, we provide a systematic design flow as well as the optimization procedure in the application of mvr-cordic algorithm. Finally, we present two VLSI architectures for the mvr-cordic algorithm. It shows that by using the proposed mvr-cordic algorithm, we can save 50% execution time in the iterative cordic structure, or 50% hardware complexity in the parallel cordic structure compared with the conventional cordic scheme.
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