On the Use of Approximate Multipliers in LMS Adaptive Filters

Abstract

Approximate computing relaxes algorithm precision constraints to improve digital circuit performance. Adaptive filters based on least-mean-square (LMS) algorithm constitute a standard in many DSP applications. The LMS algorithm, being an approximation of the Wiener filter, is inherently imprecise, and constitutes a fertile ground to employ approximate hardware techniques with the additional challenge related to the presence of a feedback path for coefficients update. In this paper, approximate LMS adaptive filters are explored for the first time, by employing approximate multipliers. A system identification scenario is adopted to assess the algorithm behavior. The analysis reveals that the choice of the approximate multiplier topology should be carefully examined, otherwise the stability and convergence performance of the algorithm can be compromised. We propose a novel approximate multiplier able to reduce the power dissipation in adaptive LMS filters up to 29% with tolerable convergence error degradation.