Skip to Main content Skip to Navigation
Conference papers

Energy Efficient Neuromorphic Computing with beyond-CMOS Oscillatory Neural Networks

Corentin Delacour 1 Stefania Carapezzi 1 Gabriele Boschetto 1 Aida Todri-Sanial 1 
1 SmartIES - Smart Integrated Electronic Systems
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
Abstract : Oscillatory Neural Networks (ONNs) are non-von Neumann architectures where information is encoded in phase relations between coupled oscillators. In this work, we present the concept of ONN based on beyond-CMOS devices to reduce the energy footprint of neuromorphic circuits. We investigate oscillating neurons made of vanadium dioxide material (VO2) and synapses based on molybdenum disulfide (MoS2) memristors to emulate synaptic plasticity.
Complete list of metadata
Contributor : Corentin Delacour Connect in order to contact the contributor
Submitted on : Wednesday, September 22, 2021 - 10:26:01 AM
Last modification on : Friday, August 5, 2022 - 3:02:16 PM
Long-term archiving on: : Thursday, December 23, 2021 - 6:25:54 PM


Files produced by the author(s)


  • HAL Id : lirmm-03229262, version 1


Corentin Delacour, Stefania Carapezzi, Gabriele Boschetto, Aida Todri-Sanial. Energy Efficient Neuromorphic Computing with beyond-CMOS Oscillatory Neural Networks. ICONS 2021 - International Conference on Neuromorphic Systems, Jul 2021, Oak Ridge (Virtual), United States. ⟨lirmm-03229262⟩



Record views


Files downloads