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Mixed-Precision Kernel Recursive Least Squares
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DOI：10.1109/TNNLS.2020.3041677.png)
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Abstract
En 中文
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 mixedprecision 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.
Keywords:
Budget machine learning
kernel method
mixed-precision training
online learning
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