Hardware efficient implementation of DFT using an improved first-order moments based cyclic convolution structure

Jun Xiong; J. G. Liu; Li Cao

doi:10.1117/12.2205718

14 December 2015 Hardware efficient implementation of DFT using an improved first-order moments based cyclic convolution structure

Jun Xiong, J. G. Liu, Li Cao

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Proceedings Volume 9814, MIPPR 2015: Parallel Processing of Images and Optimization; and Medical Imaging Processing; 981407 (2015) https://doi.org/10.1117/12.2205718
Event: Ninth International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2015), 2015, Enshi, China

Abstract

This paper presents hardware efficient designs for implementing the one-dimensional (1D) discrete Fourier transform (DFT). Once DFT is formulated as the cyclic convolution form, the improved first-order moments-based cyclic convolution structure can be used as the basic computing unit for the DFT computation, which only contains a control module, a barrel shifter and (N-1)/2 accumulation units. After decomposing and reordering the twiddle factors, all that remains to do is shifting the input data sequence and accumulating them under the control of the statistical results on the twiddle factors. The whole calculation process only contains shift operations and additions with no need for multipliers and large memory. Compared with the previous first-order moments-based structure for DFT, the proposed designs have the advantages of less hardware consumption, lower power consumption and the flexibility to achieve better performance in certain cases. A series of experiments have proven the high performance of the proposed designs in terms of the area time product and power consumption. Similar efficient designs can be obtained for other computations, such as DCT/IDCT, DST/IDST, digital filter and correlation by transforming them into the forms of the first-order moments based cyclic convolution.

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Jun Xiong, J. G. Liu, and Li Cao "Hardware efficient implementation of DFT using an improved first-order moments based cyclic convolution structure", Proc. SPIE 9814, MIPPR 2015: Parallel Processing of Images and Optimization; and Medical Imaging Processing, 981407 (14 December 2015); https://doi.org/10.1117/12.2205718

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KEYWORDS

Convolution

Clocks

Ions

Fourier transforms

Very large scale integration

Information technology

Algorithm development

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