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PARALLEL EVALUATION OF HOPFIELD NEURAL NETWORKSTopics: Neural Network Software and Applications; Stability and Instability in Artificial Neural Networks

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Abstract: Among the large number of possible optimization algorithms, Hopfield Neural Networks (HNN) propose interesting characteristics for an in-line use. Indeed, this particular optimization algorithm can produce solutions in brief delay. These solutions are produced by the HNN convergence which was originally defined for a sequential evaluation of neurons. While this sequential evaluation leads to long convergence time, we assume that this convergence can be accelerated through the parallel evaluation of neurons. However, the original constraints do not any longer ensure the convergence of the HNN evaluated in parallel. This article aims to show how the neurons can be evaluated in parallel in order to accelerate a hardware or multiprocessor implementation and to ensure the convergence. The parallelization method is illustrated on a simple task scheduling problem where we obtain an important acceleration related to the number of tasks. For instance, with a number of tasks equals to 20 the speedup factor is about 25.(More)

Among the large number of possible optimization algorithms, Hopfield Neural Networks (HNN) propose interesting characteristics for an in-line use. Indeed, this particular optimization algorithm can produce solutions in brief delay. These solutions are produced by the HNN convergence which was originally defined for a sequential evaluation of neurons. While this sequential evaluation leads to long convergence time, we assume that this convergence can be accelerated through the parallel evaluation of neurons. However, the original constraints do not any longer ensure the convergence of the HNN evaluated in parallel. This article aims to show how the neurons can be evaluated in parallel in order to accelerate a hardware or multiprocessor implementation and to ensure the convergence. The parallelization method is illustrated on a simple task scheduling problem where we obtain an important acceleration related to the number of tasks. For instance, with a number of tasks equals to 20 the speedup factor is about 25.

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Eiche, A.; Chillet, D.; Pillement, S. and Sentieys, O. (2011). PARALLEL EVALUATION OF HOPFIELD NEURAL NETWORKS. In Proceedings of the International Conference on Neural Computation Theory and Applications - NCTA, (IJCCI 2011) ISBN 978-989-8425-84-3, pages 248-253. DOI: 10.5220/0003682902480253

@conference{ncta11, author={Antoine Eiche. and Daniel Chillet. and Sebastien Pillement. and Olivier Sentieys.}, title={PARALLEL EVALUATION OF HOPFIELD NEURAL NETWORKS}, booktitle={Proceedings of the International Conference on Neural Computation Theory and Applications - NCTA, (IJCCI 2011)}, year={2011}, pages={248-253}, publisher={SciTePress}, organization={INSTICC}, doi={10.5220/0003682902480253}, isbn={978-989-8425-84-3}, }

TY - CONF

JO - Proceedings of the International Conference on Neural Computation Theory and Applications - NCTA, (IJCCI 2011) TI - PARALLEL EVALUATION OF HOPFIELD NEURAL NETWORKS SN - 978-989-8425-84-3 AU - Eiche, A. AU - Chillet, D. AU - Pillement, S. AU - Sentieys, O. PY - 2011 SP - 248 EP - 253 DO - 10.5220/0003682902480253