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arXiv · 2008.09633

Low-complexity Architecture for AR(1) Inference

A. Borges Jr.·R. J. Cintra·D. F. G. Coelho·V. S. Dimitrov
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Abstract

In this Letter, we propose a low-complexity estimator for the correlation coefficient based on the signed $\operatorname{AR}(1)$ process. The introduced approximation is suitable for implementation in low-power hardware architectures. Monte Carlo simulations reveal that the proposed estimator performs comparably to the competing methods in literature with maximum error in order of $10^{-2}$. However, the hardware implementation of the introduced method presents considerable advantages in several relevant metrics, offering more than 95% reduction in dynamic power and doubling the maximum operating frequency when compared to the reference method.

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BibTeXRIS

A. Borges Jr., R. J. Cintra, D. F. G. Coelho, V. S. Dimitrov. 2020-08-21. Low-complexity Architecture for AR(1) Inference. https://doi.org/10.1049/el.2019.4030

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