Mixed-Precision Kernel Recursive Least Squares

JunKyu Lee*, Dimitrios S. Nikolopoulos, Hans Vandierendonck

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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Abstract

Kernel recursive least squares (KRLS) is a widely used online machine learning algorithm for time series predictions. In this article, we present the mixed-precision KRLS, producing equivalent prediction accuracy to double-precision KRLS with a higher training throughput and a lower memory footprint. The mixed-precision KRLS applies single-precision arithmetic to the computation components being not only numerically resilient but also computationally intensive. Our mixed-precision KRLS demonstrates the 1.32, 1.15, 1.29, 1.09, and 1.08x training throughput improvements using 24.95%, 24.74%, 24.89%, 24.48%, and 24.20% less memory footprint without losing any prediction accuracy compared to double-precision KRLS for a 3-D nonlinear regression, a Lorenz chaotic time series, a Mackey-Glass chaotic time series, a sunspot number time series, and a sea surface temperature time series, respectively.
Original languageEnglish
JournalIEEE Transactions on Neural Networks and Learning Systems
Early online date16 Dec 2020
DOIs
Publication statusEarly online date - 16 Dec 2020

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