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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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Conference papers
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https://hal-lirmm.ccsd.cnrs.fr/lirmm-03229257
Contributor : Corentin Delacour <>
Submitted on : Tuesday, May 18, 2021 - 6:52:05 PM
Last modification on : Wednesday, September 15, 2021 - 12:12:03 PM

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  • HAL Id : lirmm-03229257, version 1

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Corentin Delacour, Stefania Carapezzi, Madeleine Abernot, Gabriele Boschetto, Nadine Azemard, et al.. Oscillatory Neural Networks for Edge AI Computing. IEEE Computer Society Annual Symposium on VLSI (ISVLSI 2021), Jul 2021, Tampa, United States. ⟨lirmm-03229257⟩

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