Asymptotically Optimal Low-Power Digital Filtering Using Adaptive Approximate Processing

Jeffrey Ludwig

doi:doi:10.11648/j.wcmc.20200802.12

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Asymptotically Optimal Low-Power Digital Filtering Using Adaptive Approximate Processing

Jeffrey Ludwig

Published in International Journal of Wireless Communications and Mobile Computing (Volume 8, Issue 2)

Received: 28 May 2021 Accepted: 15 June 2021 Published: 13 July 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.

Published in	International Journal of Wireless Communications and Mobile Computing (Volume 8, Issue 2)
DOI	10.11648/j.wcmc.20200802.12
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), 2021. 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://article.sciencepublishinggroup.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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TY  - JOUR
T1  - Asymptotically Optimal Low-Power Digital Filtering Using Adaptive Approximate Processing
AU  - Jeffrey Ludwig
Y1  - 2021/07/13
PY  - 2021
N1  - https://doi.org/10.11648/j.wcmc.20200802.12
DO  - 10.11648/j.wcmc.20200802.12
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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EP  - 38
PB  - Science Publishing Group
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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.
VL  - 8
IS  - 2
ER  -

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Author Information

Jeffrey Ludwig

Department of Mathematics, University of California, Irvine, United States of America

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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://article.sciencepublishinggroup.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}
}

Copy | Download

TY  - JOUR
T1  - Asymptotically Optimal Low-Power Digital Filtering Using Adaptive Approximate Processing
AU  - Jeffrey Ludwig
Y1  - 2021/07/13
PY  - 2021
N1  - https://doi.org/10.11648/j.wcmc.20200802.12
DO  - 10.11648/j.wcmc.20200802.12
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
SP  - 22
EP  - 38
PB  - Science Publishing Group
SN  - 2330-1015
UR  - https://doi.org/10.11648/j.wcmc.20200802.12
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.
VL  - 8
IS  - 2
ER  -

Copy | Download