Third International conference on advances in communication, network and computing

Research Article

VLSI Implementation of Burrows Wheeler Transform for Memory Reduced Distributed Arithmetic Architectures

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  • @INPROCEEDINGS{10.1007/978-3-642-35615-5_36,
        author={Remya A.S. and Lintu Rajan and Shiny C.},
        title={VLSI Implementation of Burrows Wheeler Transform for Memory Reduced Distributed Arithmetic Architectures},
        proceedings={Third International conference on advances in communication, network and computing},
        proceedings_a={CNC},
        year={2012},
        month={12},
        keywords={DWT Burrows Wheeler Transform Distributed Arithmetic Architecture Field programmable gate arrays},
        doi={10.1007/978-3-642-35615-5_36}
    }
    
  • Remya A.S.
    Lintu Rajan
    Shiny C.
    Year: 2012
    VLSI Implementation of Burrows Wheeler Transform for Memory Reduced Distributed Arithmetic Architectures
    CNC
    Springer
    DOI: 10.1007/978-3-642-35615-5_36
Remya A.S.1,*, Lintu Rajan1,*, Shiny C.1,*
  • 1: Amrita Vishwa Vidyapeetham
*Contact email: remya.amrita@gmail.com, linturajan08@gmail.com, shyni.c19@gmail.com

Abstract

Multiply and accumulate function is the important part of digital signal processing algorithms. This can be implemented more effectively with distributed arithmetic (DA) architecture [1]. These architectures make extensive use of look-up tables, which make them ideal for implementing digital signal processing functions on Xilinx FPGAs. An emerging arithmetic-intensive digital signal processing algorithm is the discrete wavelet transform (DWT) which have proven to be extremely useful for image and video coding applications like MPEG-4 and JPEG 2000[2]. But the limitation of this architecture is that the size of look-up tables get increased exponentially as the constant coefficients of wavelet used for these applications increases. In this paper, we proposed a novel methodology to implement the Burrows wheeler transform (BWT) [3] block in FPGA for achieving memory reduced DA.