Reducing quantization error in low-energy FIR filter accelerators

Zhuo Wang, Jintao Zhang, Naveen Verma

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations

Abstract

Computational energy versus computational precision represents a critical implementation-level tradeoff facing embedded DSP systems. Focusing on multiply-accumulate (MAC) hardware, which is used extensively in DSP implementations (e.g., FIR filtering), this paper proposes an approach that exploits floating-point representation of multipliers to enable optimization of their quantization error. The approach introduces a parameter α for coefficient scaling, and optimizes α to minimize the output error. Applied to FIR filters with coefficient representation of 6 bits, the approach reduces the quantization error by 37×, compared to traditional, linear-quantized fixed-point coefficient representation and by 28×, compared to unoptimized floating-point coefficient representation. Further, the energy and hardware gate-count of a MAC unit is reduced by 1.4× and 1.2×, respectively, compared to an implementation based on fixed-point representation.

Original languageEnglish (US)
Title of host publication2015 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1032-1036
Number of pages5
ISBN (Electronic)9781467369978
DOIs
StatePublished - Aug 4 2015
Event40th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015 - Brisbane, Australia
Duration: Apr 19 2014Apr 24 2014

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2015-August
ISSN (Print)1520-6149

Other

Other40th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015
CountryAustralia
CityBrisbane
Period4/19/144/24/14

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

Keywords

  • digital filter
  • embedded systems
  • floating point
  • low energy
  • quantization error

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  • Cite this

    Wang, Z., Zhang, J., & Verma, N. (2015). Reducing quantization error in low-energy FIR filter accelerators. In 2015 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015 - Proceedings (pp. 1032-1036). [7178126] (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; Vol. 2015-August). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICASSP.2015.7178126