Title :
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
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;
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
DOI :
10.1109/RADECS.1995.509817
