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Scientific Programming
Volume 5, Issue 1, Pages 47-61
http://dx.doi.org/10.1155/1996/189626

Design, Implementation, and Test of a Multi-Model Systolic Neural-Network Accelerator

Thierry Cornu,1 Paolo Ienne,1 Dagmar Niebur,2 Patrick Thiran,1 and Marc A. Viredaz3

1Swiss Federal Institute of Technology, Centre for Neuro-Mimetic Systems, IN-J Ecublens, CH-1015 Lausanne, Switzerland
2Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
3NEC Research Institute, 4 Independence Way, Princeton, NJ 08540, USA

Received 18 November 1994; Accepted 18 April 1995

Copyright © 1996 Hindawi Publishing Corporation. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

A multi-model neural-network computer has been designed and built. A compute-intensive application in the field of power-system monitoring, using the Kohonen neural network, has then been ported onto this machine. After a short description of the system, this article focuses on the programming paradigm adopted. The performance of the machine is also evaluated and discussed.