loading
Documents

Research.Publish.Connect.

Paper

Paper Unlock

Authors: Antoine Eiche ; Daniel Chillet ; Sebastien Pillement and Olivier Sentieys

Affiliation: University of Rennes I, IRISA and INRIA, France

ISBN: 978-989-8425-84-3

Keyword(s): Hopfield neural networks, Parallelization, Stability, Optimization problems.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Artificial Intelligence and Decision Support Systems ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; Enterprise Information Systems ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Methodologies and Methods ; Neural Network Software and Applications ; Neural Networks ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Signal Processing ; Soft Computing ; Stability and Instability in Artificial Neural Networks ; Theory and Methods

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 sp eedup factor is about 25. (More)

PDF ImageFull Text

Download
CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 18.206.48.142

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
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 - Volume 1: 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 - Volume 1: 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 - Volume 1: 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

Login or register to post comments.

Comments on this Paper: Be the first to review this paper.