Optimal VLSI Networks for Multidimensional Transforms

This short note presents a new class of AT₂optimal networks for computing the multidimensional Discrete Fourier Transform. Although optimal networks have been proposed previously, the networks proposed in this short note are based on a new methodology for mapping large A-shuffle networks, K ≥ 2, onto smaller area networks that maintain the optimality of the DFT network. Such networks are used to perform the index-rotation operations needed by the multidimensional computation. The resulting networks have simple regular layouts, and can be easily partitioned among several chips in order to reduce the number of input—output pins per chip.