Subthreshold neuromorphic devices for spiking neural networks applied to embedded A.I

Loyez, Christophe; Carpentier, Kevin; Sourikopoulos, Ilias; Danneville, Francois

Document type :

Communication dans un congrès avec actes

DOI :

10.1109/NEWCAS50681.2021.9462779

Title :

Subthreshold neuromorphic devices for spiking neural networks applied to embedded A.I

Author(s) :

Loyez, Christophe [Auteur]
Circuits Systèmes Applications des Micro-ondes - IEMN [CSAM - IEMN ]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]
Carpentier, Kevin [Auteur]

Société d’Accélération du Transfert de Technologie [SATT NORD]
Sourikopoulos, Ilias [Auteur]
Société d’Accélération du Transfert de Technologie [SATT NORD]
Danneville, Francois [Auteur]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]
Advanced NanOmeter DEvices - IEMN [ANODE - IEMN]

Conference title :

19th IEEE International New Circuits and Systems Conference, NEWCAS 2021

City :

Toulon

Country :

France

Start date of the conference :

2021-06-13

Publisher :

IEEE

HAL domain(s) :

Sciences de l'ingénieur [physics]

English abstract : [en]

Energy autonomy is one of the major challenges of embedded Artificial Intelligence. Among the candidate technologies likely to take up such a challenge, spiking neural networks are the most promising because of both their ...
Show more >Energy autonomy is one of the major challenges of embedded Artificial Intelligence. Among the candidate technologies likely to take up such a challenge, spiking neural networks are the most promising because of both their spatio-temporal and sparse representation of the information. In this context, this paper presents a neuromorphic approach based on an industrial CMOS technology and adopting an entirely subthreshold mode of operation (supply voltage VDD lower than the MOSFET threshold voltage). The detailed topologies of fabricated artificial neurons and synapses are presented as well as experimental results, validating an energy consumption of the order of a few femto-Joules per spike. Also, an arrangement of neurons and synapses is proposed to qualify experimentally this subthreshold approach in the perspective of highly energy efficient spiking neural networks.Show less >

Language :

Anglais

Peer reviewed article :

Oui

Audience :

Internationale

Popular science :

Non

Collections :

Institut d'Électronique, de Microélectronique et de Nanotechnologie (IEMN) - UMR 8520

Source :

Harvested from HAL

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