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Exploiting Parallelism in Neural Networks on a Dynamic Data-Driven System
Ali M. ALHAJ Hiroaki TERADA
IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences
Publication Date: 1993/10/25
Print ISSN: 0916-8508
Type of Manuscript: PAPER
Category: Neural Networks
data-driven systems, neural networks, back propagation, parallelism exploitation, parallel simulation,
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High speed simulation of neural networks can be achieved through parallel implementations capable of exploiting their massive inherent parallelism. In this paper, we show how this inherent parallelism can be effectively exploited on parallel data-driven systems. By using these systems, the asynchronous parallelism of neural networks can be naturally specified by the functional data-driven programs, and maximally exploited by pipelined and scalable data-driven processors. We shall demonstrate the suitability of data-driven systems for the parallel simulation of neural networks through a parallel implementation of the widely used back propagation networks. The implementation is based on the exploitation of the network and training set parallelisms inherent in these networks, and is evaluated using an image data compression network.