Oscillatory Neural Networks for Edge AI Computing - LIRMM - Laboratoire d’Informatique, de Robotique et de Microélectronique de Montpellier Access content directly
Conference Papers Year : 2021

Oscillatory Neural Networks for Edge AI Computing


In this paper, we showcase the innovative concept of implementing Oscillatory Neural Networks (ONNs) for neuromorphic computing with beyond CMOS devices based on vanadium dioxide to mimic neurons and resistors to emulate synapses. We explore ONN technology potentials from device to analog circuit-level simulations. We report that ONN behaves like an associative memory and can implement energy-based models such as Hopfield Neural Networks on edge devices. Finally, as a proof of concept, a reconfigurable digital ONN is implemented on FPGA for pattern recognition tasks.
Fichier principal
Vignette du fichier
ISVLSI_eXpress.pdf (1019.54 Ko) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

lirmm-03229257 , version 1 (22-09-2021)



Corentin Delacour, Stefania Carapezzi, Madeleine Abernot, Gabriele Boschetto, Nadine Azemard, et al.. Oscillatory Neural Networks for Edge AI Computing. ISVLSI 2021 - IEEE Computer Society Annual Symposium on VLSI, Jul 2021, Tampa, United States. pp.326-331, ⟨10.1109/ISVLSI51109.2021.00066⟩. ⟨lirmm-03229257⟩
175 View
625 Download



Gmail Facebook X LinkedIn More