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EU H2020 NEURONN: 2D Oscillatory Neural Networks For Energy Efficient Neuromorphic Computing

Stefania Carapezzi 1 Gabriele Boschetto 1 Corentin Delacour 1 Madeleine Abernot 1 Thierry Gil 1 Aida Todri-Sanial 1
1 SmartIES - Smart Integrated Electronic Systems
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
Abstract : In this paper, we showcase a leading-edge implementation of oscillatory neural networks (ONNs) using beyond Complementary-Metal-Oxide-Semiconductor devices based on vanadium dioxide to mimick neurons, and 2D molybdenum disulfide memristors to emulate synapses. We explore the ONN technology through simulations from materials to devices up to circuits. We show that ONNs naturally behave like associative memories and can be used for pattern recognition, a task to be exploited in edge devices. Finally, we develop a reconfigurable digital ONN-on-FPGA to assess ONN functionality in real world applications.
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https://hal-lirmm.ccsd.cnrs.fr/lirmm-03270397
Contributor : Stefania Carapezzi Connect in order to contact the contributor
Submitted on : Wednesday, September 22, 2021 - 7:25:45 PM
Last modification on : Friday, October 22, 2021 - 3:07:43 PM

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  • HAL Id : lirmm-03270397, version 1

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Stefania Carapezzi, Gabriele Boschetto, Corentin Delacour, Madeleine Abernot, Thierry Gil, et al.. EU H2020 NEURONN: 2D Oscillatory Neural Networks For Energy Efficient Neuromorphic Computing. 15ème Colloque National du GDR SoC², Jun 2021, Rennes, France. 2021. ⟨lirmm-03270397⟩

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