Distributed Clique-Based Neural Networks ...
Type de document :
Communication dans un congrès avec actes
Titre :
Distributed Clique-Based Neural Networks for Data Fusion at the Edge
Auteur(s) :
Larras, Benoit [Auteur]
Microélectronique Silicium - IEMN [MICROELEC SI - IEMN]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]
Frappe, Antoine [Auteur]
Microélectronique Silicium - IEMN [MICROELEC SI - IEMN]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]
Microélectronique Silicium - IEMN [MICROELEC SI - IEMN]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]
Frappe, Antoine [Auteur]
Microélectronique Silicium - IEMN [MICROELEC SI - IEMN]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]
Titre de la manifestation scientifique :
2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS)
Ville :
Genova
Pays :
Italie
Date de début de la manifestation scientifique :
2020-08-31
Titre de la revue :
Proceedings of the 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS 2020)
Éditeur :
IEEE
Date de publication :
2020
Discipline(s) HAL :
Sciences de l'ingénieur [physics]
Résumé en anglais : [en]
Distributed smart sensors are more and more used in applications such as biomedical or domestic monitoring. However, each sensor broadcasts data wirelessly to the others or to an aggregator, which leads to energy-hungry ...
Lire la suite >Distributed smart sensors are more and more used in applications such as biomedical or domestic monitoring. However, each sensor broadcasts data wirelessly to the others or to an aggregator, which leads to energy-hungry sensors not ensuring data privacy. To tackle both challenges, this work proposes to distribute a part of a clique-based neural network in each sensor. This scheme allows standardizing data at the sensor level, ensuring privacy if the data is intercepted. Besides, a lower number of bits is transmitted, thus limiting the communication overhead. The circuit implementation is possible with the use of single-cluster iterative clique-based circuits. To this end, a hardware circuit has been fabricated and performs a classification using 115fJ per synaptic event per neuron in 83ns.Lire moins >
Lire la suite >Distributed smart sensors are more and more used in applications such as biomedical or domestic monitoring. However, each sensor broadcasts data wirelessly to the others or to an aggregator, which leads to energy-hungry sensors not ensuring data privacy. To tackle both challenges, this work proposes to distribute a part of a clique-based neural network in each sensor. This scheme allows standardizing data at the sensor level, ensuring privacy if the data is intercepted. Besides, a lower number of bits is transmitted, thus limiting the communication overhead. The circuit implementation is possible with the use of single-cluster iterative clique-based circuits. To this end, a hardware circuit has been fabricated and performs a classification using 115fJ per synaptic event per neuron in 83ns.Lire moins >
Langue :
Anglais
Comité de lecture :
Oui
Audience :
Internationale
Vulgarisation :
Non
Source :