International Journal of Wireless Communications and Mobile Computing

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Asymptotically Optimal Low-Power Digital Filtering Using Adaptive Approximate Processing

Received: May 28, 2021    Accepted: Jun. 15, 2021    Published: Jul. 13, 2021
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Abstract

Techniques for reducing power consumption in digital circuits have become increasingly important because of the growing demand for portable multimedia devices. Digital filters, being ubiquitous in such devices, are a prime candidate for low-power design. We present a new algorithmic approach to low-power frequency-selective digital filtering which is based on the concepts of adaptive approximate processing. This approach is formalized by introducing the class of approximate filtering algorithms in which the order of a digital filter is dynamically varied to provide time-varying stopband attenuation in proportion to the time-varying signal-to-noise ratio (SNR) of the input signal, while maintaining a fixed SNR at the filter output. Since power consumption in digital filter implementations is proportional to the order of the filter, dynamically varying the filter order is a strategy which may be used to conserve power. From this practical technique we abstract a theoretical problem which involves the determination of an optimal filter order based on observations of the input data and a set of concrete assumptions on the statistics of the input signal. Two solutions to this theoretical problem are presented, and the key results are used to interpret the solution to the practical low-power filtering problem. We construct a framework to explore the statistical properties of approximate filtering algorithms and show that under certain assumptions, the performance of approximate filtering algorithms is asymptotically optimal.

DOI 10.11648/j.wcmc.20200802.12
Published in International Journal of Wireless Communications and Mobile Computing ( Volume 8, Issue 2, December 2020 )
Page(s) 22-38
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Low-power Signal Processing, Adaptive Filtering, Approximate Signal Processing

References
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  • APA Style

    Jeffrey Ludwig. (2021). Asymptotically Optimal Low-Power Digital Filtering Using Adaptive Approximate Processing. International Journal of Wireless Communications and Mobile Computing, 8(2), 22-38. https://doi.org/10.11648/j.wcmc.20200802.12

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    ACS Style

    Jeffrey Ludwig. Asymptotically Optimal Low-Power Digital Filtering Using Adaptive Approximate Processing. Int. J. Wirel. Commun. Mobile Comput. 2021, 8(2), 22-38. doi: 10.11648/j.wcmc.20200802.12

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    AMA Style

    Jeffrey Ludwig. Asymptotically Optimal Low-Power Digital Filtering Using Adaptive Approximate Processing. Int J Wirel Commun Mobile Comput. 2021;8(2):22-38. doi: 10.11648/j.wcmc.20200802.12

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  • @article{10.11648/j.wcmc.20200802.12,
      author = {Jeffrey Ludwig},
      title = {Asymptotically Optimal Low-Power Digital Filtering Using Adaptive Approximate Processing},
      journal = {International Journal of Wireless Communications and Mobile Computing},
      volume = {8},
      number = {2},
      pages = {22-38},
      doi = {10.11648/j.wcmc.20200802.12},
      url = {https://doi.org/10.11648/j.wcmc.20200802.12},
      eprint = {https://download.sciencepg.com/pdf/10.11648.j.wcmc.20200802.12},
      abstract = {Techniques for reducing power consumption in digital circuits have become increasingly important because of the growing demand for portable multimedia devices. Digital filters, being ubiquitous in such devices, are a prime candidate for low-power design. We present a new algorithmic approach to low-power frequency-selective digital filtering which is based on the concepts of adaptive approximate processing. This approach is formalized by introducing the class of approximate filtering algorithms in which the order of a digital filter is dynamically varied to provide time-varying stopband attenuation in proportion to the time-varying signal-to-noise ratio (SNR) of the input signal, while maintaining a fixed SNR at the filter output. Since power consumption in digital filter implementations is proportional to the order of the filter, dynamically varying the filter order is a strategy which may be used to conserve power. From this practical technique we abstract a theoretical problem which involves the determination of an optimal filter order based on observations of the input data and a set of concrete assumptions on the statistics of the input signal. Two solutions to this theoretical problem are presented, and the key results are used to interpret the solution to the practical low-power filtering problem. We construct a framework to explore the statistical properties of approximate filtering algorithms and show that under certain assumptions, the performance of approximate filtering algorithms is asymptotically optimal.},
     year = {2021}
    }
    

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    T2  - International Journal of Wireless Communications and Mobile Computing
    JF  - International Journal of Wireless Communications and Mobile Computing
    JO  - International Journal of Wireless Communications and Mobile Computing
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    AB  - Techniques for reducing power consumption in digital circuits have become increasingly important because of the growing demand for portable multimedia devices. Digital filters, being ubiquitous in such devices, are a prime candidate for low-power design. We present a new algorithmic approach to low-power frequency-selective digital filtering which is based on the concepts of adaptive approximate processing. This approach is formalized by introducing the class of approximate filtering algorithms in which the order of a digital filter is dynamically varied to provide time-varying stopband attenuation in proportion to the time-varying signal-to-noise ratio (SNR) of the input signal, while maintaining a fixed SNR at the filter output. Since power consumption in digital filter implementations is proportional to the order of the filter, dynamically varying the filter order is a strategy which may be used to conserve power. From this practical technique we abstract a theoretical problem which involves the determination of an optimal filter order based on observations of the input data and a set of concrete assumptions on the statistics of the input signal. Two solutions to this theoretical problem are presented, and the key results are used to interpret the solution to the practical low-power filtering problem. We construct a framework to explore the statistical properties of approximate filtering algorithms and show that under certain assumptions, the performance of approximate filtering algorithms is asymptotically optimal.
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Author Information
  • Department of Mathematics, University of California, Irvine, United States of America

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