InforLorV4, Main, Exploration, bibRecord, 005201

FPGA-targeted neural architecture for embedded alertness detection

Identifieur interne : 005201 ( Main/Exploration ); précédent : 005200; suivant : 005202

FPGA-targeted neural architecture for embedded alertness detection

Auteurs : Bernard Girau [France] ; Khaled Ben Khalifa

Source :

RBID : Hal:inria-00102741

English descriptors

mix :
- FPGA, FPNA, alertness, low-power, neural networks.

Abstract

Several recent works have used neural networks to discrim inate vigilance states in humans from electroencephalo graphic (EEG) signals. Our study aims at being more ex haustive. It takes into account various connectionist mod els, and it precisely studies their features and their perfor mances. Physicians have been associated to the project, especially when tuning our models. Above all, our work has been oriented in such a way to get a light, low-power, easy to wear system. First implementation works have fo cused on the use of a Self-Organizing Map architecture, since the most efﬁcient neural model of our study, a multi layer perceptron (MLP), was too huge for a straightforward FPGA implementation. In this paper, we describe how the theory of FPNA (Field Programmable Neural Arrays) has been applied to this model, so as to simplify the topology of the MLP of our application. Thanks to this simpliﬁca tion, a fully parallel FPGA implementation has been made possible and efﬁcient, without any signiﬁcant performance loss.

Url:

https://hal.inria.fr/inria-00102741

Affiliations:

Links toward previous steps (curation, corpus...)

to stream Hal, to step Corpus: 002200
to stream Hal, to step Curation: 002200
to stream Hal, to step Checkpoint: 004142
to stream Main, to step Merge: 005367
to stream Main, to step Curation: 005201

Le document en format XML

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<front><div type="abstract" xml:lang="en">Several recent works have used neural networks to discrim inate vigilance states in humans from electroencephalo graphic (EEG) signals. Our study aims at being more ex haustive. It takes into account various connectionist mod els, and it precisely studies their features and their perfor mances. Physicians have been associated to the project, especially when tuning our models. Above all, our work has been oriented in such a way to get a light, low-power, easy to wear system. First implementation works have fo cused on the use of a Self-Organizing Map architecture, since the most efﬁcient neural model of our study, a multi layer perceptron (MLP), was too huge for a straightforward FPGA implementation. In this paper, we describe how the theory of FPNA (Field Programmable Neural Arrays) has been applied to this model, so as to simplify the topology of the MLP of our application. Thanks to this simpliﬁca tion, a fully parallel FPGA implementation has been made possible and efﬁcient, without any signiﬁcant performance loss.</div>
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FPGA-targeted neural architecture for embedded alertness detection

FPGA-targeted neural architecture for embedded alertness detection

Source :

English descriptors

Abstract

Links toward previous steps (curation, corpus...)

Le document en format XML

Pour manipuler ce document sous Unix (Dilib)

Pour mettre un lien sur cette page dans le réseau Wicri