FPGA architecture for 2d discrete Fourier transform based on 2d decomposition for large-sized data

Chi Li Yu, Jung Sub Kim, Lanping Deng, Srinidhi Kestur, Vijaykrishnan Narayanan, Chaitali Chakrabarti

Research output: Contribution to journalArticle

12 Scopus citations

Abstract

Applications based on Discrete Fourier Transforms (DFT) are extensively used in several areas of signal and digital image processing. Of particular interest is the two-dimensional (2D) DFT which is more computation-and bandwidth-intensive than the one-dimensional (1D) DFT. Traditionally, a 2D DFT is computed using Row-Column (RC) decomposition, where 1D DFTs are computed along the rows followed by 1D DFTs along the columns. Both application specific and reconfigurable hardware have utilized this scheme for high-performance implementations of 2D DFT. However, architectures based on RC decomposition are not efficient for large input size data due to memory bandwidth constraints. In this paper, we propose an efficient architecture to implement 2D DFT for large-sized input data based on a novel 2D decomposition algorithm. This architecture achieves very high throughput by exploiting the inherent parallelism due to the algorithm decomposition and by utilizing the row-wise burst access pattern of the external memory. A high throughput memory interface has been designed to enable maximum utilization of the memory bandwidth. In addition, an automatic system generator is provided for mapping this architecture onto a reconfigurable platform of Xilinx Virtex-5 devices. For a 2K ×2K input size, the proposed architecture is 1.96 times faster than RC decomposition based implementation under the same memory constraints, and also outperforms other existing implementations.

Original languageEnglish (US)
Pages (from-to)109-122
Number of pages14
JournalJournal of Signal Processing Systems
Volume64
Issue number1
DOIs
StatePublished - Jul 1 2011

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Theoretical Computer Science
  • Signal Processing
  • Information Systems
  • Modeling and Simulation
  • Hardware and Architecture

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