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Oscillatory Neural Networks for Edge AI Computing

Abstract : 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.
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https://hal-lirmm.ccsd.cnrs.fr/lirmm-03229257
Contributor : Corentin Delacour Connect in order to contact the contributor
Submitted on : Wednesday, September 22, 2021 - 3:26:24 PM
Last modification on : Friday, August 5, 2022 - 3:02:16 PM
Long-term archiving on: : Thursday, December 23, 2021 - 6:53:58 PM

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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⟩

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