Robustness against SEU of an artificial neural network space application

Robustness against SEU of an artificial neural network space application

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 te...

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Veröffentlicht in:IEEE Transactions on Nuclear Science 1996-06, Vol.43 (3), p.973-978
Hauptverfasser: Assoum, A., Radi, N.E., Velazco, R., Elie, F., Ecoffet, R.
Format: Artikel
Sprache:eng
Schlagworte:
Artificial intelligence
Artificial neural networks
COMPUTERIZED SIMULATION
ERRORS
EXPERIMENTAL DATA
Fault tolerance
Hardware
INSTRUMENTATION, INCLUDING NUCLEAR AND PARTICLE DETECTORS
INTEGRATED CIRCUITS
IONIZING RADIATIONS
NEURAL NETWORKS
Neurons
Noise robustness
PHYSICAL RADIATION EFFECTS
Q1
Satellites
Signal processing algorithms
Single event upset
SPACE FLIGHT
THEORETICAL DATA
Very large scale integration
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Beschreibung
Zusammenfassung: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.
ISSN:0018-9499
1558-1578
DOI:10.1109/23.510742