Robustness against S.E.U. of an artificial neural network space application

Author

Assoum, A. ; Radi, N.E. ; Velazco, R. ; Elie, F. ; Ecoffet, R.

Author_Institution

Lab. de Genie Inf., IMAG, Grenoble, France

fYear

1995

fDate

18-22 Sep 1995

Firstpage

443

Lastpage

448

Abstract

We study the sensitivity of Artificial Neural Networks (ANN) to Single Event Upsets (SEU). A neural network designed to detect electronic and protonic whistlers has been implemented using a dedicated VLSI circuit: the LNeuro neural processor. Results of both SEU software simulations and heavy ion tests point out the fault tolerance properties of ANN hardware implementations

Keywords

VLSI; ion beam effects; neural chips; space vehicle electronics; whistlers; ANN hardware; LNeuro neural processor; SEU robustness; VLSI circuit; artificial neural network; electronic whistler detection; fault tolerance; heavy ion testing; protonic whistler detection; single event upsets; software simulation; space application; Artificial neural networks; Fault tolerance; Hardware; Neural networks; Neurons; Noise robustness; Satellites; Signal processing algorithms; Single event upset; Very large scale integration;

fLanguage

English

Publisher

ieee

Conference_Titel

Radiation and its Effects on Components and Systems, 1995. RADECS 95., Third European Conference on

Conference_Location

Arcachon

Print_ISBN

0-7803-3093-5

Type

conf

DOI

10.1109/RADECS.1995.509817

Filename

509817

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2201192