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
In the paper a new neural network based out-of-step protection scheme is presented. Numerous ANNs ha...
ABSTRACT In a test setup, a hardware neural network determined track parameters of charged particles...
High Energy Physics (HEP) experiments require discrimination of a few interesting events among a hug...
The SEU sensitivity of an Artificial Neural Network intended to be used in space to detect "protonic...
International audienceThis paper investigates the tolerance of Artificial Neural Networks with respe...
In this paper we investigate the robustness of Artificial Neural Networks when encountering transien...
International audienceWe study the sensitivity of an Artificial Neural Network designed to classify ...
The sensitivity to faults induced by radiation of an artificial neural network intended to be used i...
With the increasing complexity of treatments on satellite-borne and the utilisation of highly integr...
The Microelectronics and Photonics Testbed (MPTB) carrying twenty-four experiments on-board a scient...
The paper presents some results of research work in the field of artificial neural networks (ANN) ap...
International audienceArtificial neural networks have been shown to possess fault tolerant propertie...
Artificial Neural Networks have been shown to possess fault tolerant properties. We present the arch...
ISBN : 978-1-4673-5306-9International audienceThe associative Hopfield memory is a form of recurrent...
The application of Artificial Neural Networks in Particle Physics is reviewed. Most common is the us...
In the paper a new neural network based out-of-step protection scheme is presented. Numerous ANNs ha...
ABSTRACT In a test setup, a hardware neural network determined track parameters of charged particles...
High Energy Physics (HEP) experiments require discrimination of a few interesting events among a hug...
The SEU sensitivity of an Artificial Neural Network intended to be used in space to detect "protonic...
International audienceThis paper investigates the tolerance of Artificial Neural Networks with respe...
In this paper we investigate the robustness of Artificial Neural Networks when encountering transien...
International audienceWe study the sensitivity of an Artificial Neural Network designed to classify ...
The sensitivity to faults induced by radiation of an artificial neural network intended to be used i...
With the increasing complexity of treatments on satellite-borne and the utilisation of highly integr...
The Microelectronics and Photonics Testbed (MPTB) carrying twenty-four experiments on-board a scient...
The paper presents some results of research work in the field of artificial neural networks (ANN) ap...
International audienceArtificial neural networks have been shown to possess fault tolerant propertie...
Artificial Neural Networks have been shown to possess fault tolerant properties. We present the arch...
ISBN : 978-1-4673-5306-9International audienceThe associative Hopfield memory is a form of recurrent...
The application of Artificial Neural Networks in Particle Physics is reviewed. Most common is the us...
In the paper a new neural network based out-of-step protection scheme is presented. Numerous ANNs ha...
ABSTRACT In a test setup, a hardware neural network determined track parameters of charged particles...
High Energy Physics (HEP) experiments require discrimination of a few interesting events among a hug...