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Modified Sliding-Block Distributed Arithmetic with Offset Binary Coding for Adaptive Filters

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Abstract

An efficient way for computing the response of an adaptive digital filter is to use sliding-block distributed arithmetic (SBDA). However, a disadvantage of distributed arithmetic is the amount of memory utilized. By encoding the memory tables in offset binary code (OBC), the size of the memory tables is reduced by half, while the computational workload remains unchanged. By modifying the computational flow, the computational workload can be reduced by almost by half at the expense of slightly more memory. This modified SBDA structure is called SBDA-OBC. It has memory requirements 25–50% lower than SBDA depending on the size of the sub-filter. In terms of the computational workload, SBDA-OBC is most advantageous for large sub-filters and when the filter is split into few sub-filters. In this case, the computational workload is reduced almost in half.

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References

  1. Jones, D. L. (1993). Efficient computation of time-varying and adaptive filters. IEEE Transactions on Signal Processing, 41, 1077–1086.

    Article  MATH  Google Scholar 

  2. Hwang, K. (1979). Computer arithmetic: Principles, architecture, and design (1st ed.). New York: Wiley.

    Google Scholar 

  3. Peled, A., & Liu, B. (1974). A new hardware realization of digital filters. IEEE Transactions on Acoustics, Speech, and Signal Processing, 22, 456–462.

    Article  Google Scholar 

  4. White, S. A. (1989). Applications of distributed arithmetic to digital signal processing: A tutorial review. IEEE ASSP Magazine, 6, 4–19.

    Article  Google Scholar 

  5. Cowan, C. F. N., & Mavor, J. (1981). New digital adaptive-filter implementation using distributed-arithmetic techniques. IEE Proceedings, Part F: Communications, Radar and Signal Processing, 128, 225–230.

    Article  Google Scholar 

  6. Cowan, C. F. N., Smith, S. G., & Elliott, J. H. (1983). A digital adaptive filter using a memory-accumulator architecture: Theory and realization. IEEE Transactions on Acoustics, Speech, and Signal Processing, 31, 541–549.

    Article  Google Scholar 

  7. Wei, C. H., & Lou, J. J. (1986). Multimemory block structure for implementing a digital adaptive filter using distributed arithmetic. IEE Proceedings, Part G: Electronic Circuits and Systems, 133, 19–26.

    Article  Google Scholar 

  8. Allred, D. J., Yoo, H., Krishnan, V., Huang, W., & Anderson, D. V. (2005). LMS adaptive filters using distributed arithmetic for high throughput. IEEE Transactions on Circuits and Systems, 52(7), 1327–1337.

    Article  Google Scholar 

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Authors and Affiliations

  1. School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, 30332-0250, USA

    Walter Huang & David V. Anderson

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  1. Walter Huang
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  2. David V. Anderson
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Correspondence to Walter Huang.

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Huang, W., Anderson, D.V. Modified Sliding-Block Distributed Arithmetic with Offset Binary Coding for Adaptive Filters. J Sign Process Syst 63, 153–163 (2011). https://doi.org/10.1007/s11265-010-0479-4

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  • DOI: https://doi.org/10.1007/s11265-010-0479-4

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