Publication: A Fully Parallel Implementation of a Scalable Hopfield Neural Network on the SHARC-NET Supercomputer

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Title	A Fully Parallel Implementation of a Scalable Hopfield Neural Network on the SHARC-NET Supercomputer
Authors/Editors*	Edward R. Sykes, Alexander Mirkovic
Where published*	The 19th International Symposium on High Performance Computing Systems and Applications
How published*	Proceedings
Year*	2005
Volume	5
Number	2
Pages	214-220
Publisher	IEEE
Keywords
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Abstract	Artificial neural networks (ANN) are an established area of artificial intelligence (AI) and computer science. ANNs have been used in a number of ways for research and industrial projects. However, despite ANN research spanning many years, the typical implementation is a single threaded programming model. This paper presents a fully parallel implementation of a Hopfield Neural Network using a supercomputer. The goal of this project is to develop a core learning unit capable of enormous range of scaling ability over a large number of nodes in a supercomputer. Furthermore, we integrate techniques that minimize the dependencies on any particular topology thus making it easier to port to other supercomputing environments. Ideally, other SHARC-net users will extend these ideas and conduct research using the tools developed in this project. This paper provides an outline of the issues associated with the development of this artificial neural network on SHARC-net, the benefits of such work, the difficulties encountered and future directions.