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Two-Dimensional Oscillatory Neural Networks for Energy Efficient Neuromorphic Computing

Abstract : Two-dimensional oscillatory neural networks for energy efficient neuromorphic computing The 4 th and 5 th of February 2020, in Montpellier (France), at the premises of LIRMM, CNRS the Kick-off meeting of NeurONN took place. All the Partners of the NeurONN Consortium met and set the ground for the activities along the three-year duration of the EU Project. NeurONN 1 is a research project funded by H2020 EU's research and innovation programme with core subject "Energy-efficient bio-inspired devices accelerate route to brain-like computing". The project with duration of 36 months (1 January 2020-31 December 2022) brings together leading European research and academic institutions. Neuro-inspired computing employs technologies that enable brain-inspired computing hardware for more efficient and adaptive intelligent systems. Mimicking the human brain and nervous system, these computing architectures are excellent candidates for solving complex and large-scale associative learning problems. The EU-funded NeurONN project will showcase a novel and alternative neuromorphic computing paradigm based on energy-efficient devices and architectures. In the proposed neuro-inspired computing architecture, information will be encoded in the phase of coupled oscillating neurons or oscillatory neural networks (ONN).
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Contributor : Eirini Karachristou Connect in order to contact the contributor
Submitted on : Friday, April 3, 2020 - 12:14:03 PM
Last modification on : Tuesday, October 19, 2021 - 12:49:46 PM


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  • HAL Id : lirmm-02530086, version 1


Aida Todri-Sanial, Thierry Gil, Nadine Azemard, Jérémie Salles, Stefania Carapezzi, et al.. Two-Dimensional Oscillatory Neural Networks for Energy Efficient Neuromorphic Computing. EU H2020 ICT NEURONN Research Project, 2020. ⟨lirmm-02530086⟩



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