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

Authors:
Thierry Cornu;Paolo Ienne;Dagmar Niebur;Patrick Thiran;Marc A. Viredaz
Affiliations:
Swiss Federal Institute of Technology, Centre for Neuro-Mimetic Systems, IN-J Ecublens, CH-1015 Lausanne, Switzerland/ e-mail: {cornu.ienne.thiran}@epfl.ch;Swiss Federal Institute of Technology, Centre for Neuro-Mimetic Systems, IN-J Ecublens, CH-1015 Lausanne, Switzerland/ e-mail: {cornu.ienne.thiran}@epfl.ch;Now with Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA/ e-mail: niebur@telerobotics.jpl.nasa.gov;Swiss Federal Institute of Technology, Centre for Neuro-Mimetic Systems, IN-J Ecublens, CH-1015 Lausanne, Switzerland/ e-mail: {cornu.ienne.thiran}@epfl.ch;Now with NEC Research Institute, 4 Independence Way, Princeton, NJ 08540/ e-mail: viredaz@research.nj.nec.com
Venue:
Scientific Programming - Parallel Computing Projects of the Swiss Priority Programme
Year:
1996

Citing 7
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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.