Title :

A fully flexible circuit implementation of clique-based neural networks in 65-nm CMOS

Author(s) :

Larras, Benoit [Auteur]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]
Microélectronique Silicium - IEMN [MICROELEC SI - IEMN]
Chollet, Paul [Auteur]
Lab-STICC_IMTA_CACS_IAS
Département Electronique [IMT Atlantique - ELEC]
Lahuec, Cyril [Auteur]
Lab-STICC_IMTA_CACS_IAS
Département Electronique [IMT Atlantique - ELEC]
Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance [Lab-STICC]
Seguin, Fabrice [Auteur]
Lab-STICC_IMTA_CACS_IAS
Département Electronique [IMT Atlantique - ELEC]
Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance [Lab-STICC]
Arzel, Matthieu [Auteur]
Lab-STICC_IMTA_CACS_IAS
Département Electronique [IMT Atlantique - ELEC]
Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance [Lab-STICC]

Conference title :

ISCAS 2018 : IEEE International Symposium on Circuits and Systems (ISCAS)

City :

Firenze

Country :

Italie

Start date of the conference :

2018-05-27

Book title :

Proceedings ISCAS 2018 : IEEE International Symposium on Circuits and Systems (ISCAS)

Publication date :

2018

English keyword(s) :

Analogue integrated circuit
Neural Networks
Energy effciency

HAL domain(s) :

Sciences de l'ingénieur [physics]/Electronique
Sciences de l'ingénieur [physics]/Micro et nanotechnologies/Microélectronique
Sciences de l'ingénieur [physics]/Traitement du signal et de l'image [eess.SP]

English abstract : [en]

Clique-based neural networks implement low- complexity functions working with a reduced connectivity be- tween neurons. Thus, they address very specific applications operating with a very low energy budget. This paper ...
Show more >Clique-based neural networks implement low- complexity functions working with a reduced connectivity be- tween neurons. Thus, they address very specific applications operating with a very low energy budget. This paper proposes a flexible and iterative neural architecture able to implement multiple types of clique-based neural networks of up to 3968 neurons. The circuit has been integrated in a ST 65-nm CMOS ASIC and validated in the context of ECG classification. The network core reacts in 83ns to a stimulation and occupies a 0.21mm 2 silicon area.Show less >

Language :

Anglais

Peer reviewed article :

Oui

Audience :

Internationale

Popular science :

Non

ANR Project :

Unité de pré-traitement pour systèmes intégrés à faible énergie

Collections :

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

Source :

Harvested from HAL