Paper Info
Title
Parallel Evaluation of Hopfield Neural Networks.
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.
Year
Venue
Keywords
2011
NCTA 2011: PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON NEURAL COMPUTATION THEORY AND APPLICATIONS
Hopfield neural networks,Parallelization,Stability,Optimization problems
Field
DocType
Citations 
Pattern recognition,Computer science,Types of artificial neural networks,Artificial intelligence,Artificial neural network,Hopfield network,Cellular neural network
Conference
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Antoine Eiche1323.03
Daniel Chillet219326.12
Sébastien Pillement310017.33
Olivier Sentieys459773.35