EU H2020 NEURONN: 2D Oscillatory Neural Networks For Energy Efficient Neuromorphic Computing - LIRMM - Laboratoire d’Informatique, de Robotique et de Microélectronique de Montpellier Access content directly
Conference Poster Year : 2021

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

Corentin Delacour
Thierry Gil
Aida Todri-Sanial

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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Dates and versions

lirmm-03270397 , version 1 (22-09-2021)

Identifiers

  • HAL Id : lirmm-03270397 , version 1

Cite

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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