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Low-Power Hardware Implementation of Least-Mean-Square Adaptive Filters Using Approximate Arithmetic

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Abstract

Adaptive filters based on least-mean-square (LMS) algorithm are used in several applications in virtue of their good steady-state performance, numerical stability, and acceptable computational complexity. The hardware implementation of LMS filters requires a massive number of multipliers that significantly impact on the power consumption. Approximate computing, a design technique that trades off computation accuracy for better electrical performance, is a way to improve the energy efficiency of LMS filters. In this paper, we implement state-of-the-art approximate multipliers and evaluate their impact on the performance of the LMS algorithm. Moreover, a novel approximate multiplier, whose accuracy can be tuned at design time to better adapt to the application scenario, is proposed. Implementation results in 28-nm CMOS technology allow us to investigate the power versus quality trade-off of the considered LMS approximate filters in two different applications.

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Authors and Affiliations

  1. Department of Electrical Engineering and Information Technology, University of Napoli Federico, 80138, Naples, II, Italy

    Darjn Esposito, Davide De Caro, Gennaro Di Meo, Ettore Napoli & Antonio G. M. Strollo

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  1. Darjn Esposito
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  2. Davide De Caro
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  3. Gennaro Di Meo
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  4. Ettore Napoli
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  5. Antonio G. M. Strollo
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Correspondence to Darjn Esposito.

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Esposito, D., De Caro, D., Di Meo, G. et al. Low-Power Hardware Implementation of Least-Mean-Square Adaptive Filters Using Approximate Arithmetic. Circuits Syst Signal Process 38, 5606–5622 (2019). https://doi.org/10.1007/s00034-019-01132-y

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