Title

Parallel Bayesian ARTMAP and Its OpenCL Implementation

Document Type

Article

Department or Administrative Unit

Computer Science

Publication Date

7-6-2017

Abstract

The Bayesian ARTMAP neural network, introduced by Vigdor and Lerner, is an incremental learning algorithm which can efficiently process massive datasets for classification, regression, and probabilistic inference tasks. We introduce the parallelized version of the BA neural network and implement it in OpenCL. Our implementation runs on both multi-core CPUs and GPUs architectures. We test the Parallel Bayesian ARTMAP on several classification and regression benchmarks focusing on speedup and scalability. In some cases, the parallel BA runs by an order of magnitude faster than the sequential implementation. Our implementation has the potential to scale for OpenCL devices with increasing number of compute units.

Comments

This article was originally published in Neural Processing Letters. The full-text article from the publisher can be found here.

Due to copyright restrictions, this article is not available for free download from ScholarWorks @ CWU.

Journal

Neural Processing Letters

Rights

Copyright © 2017, Springer Science Business Media, LLC

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