Two-Dimensional Oscillatory Neural Networks for Energy Efficient Neuromorphic Computing - LIRMM - Laboratoire d’Informatique, de Robotique et de Microélectronique de Montpellier Access content directly
Other Publications Year : 2020

Two-Dimensional Oscillatory Neural Networks for Energy Efficient Neuromorphic Computing

Aida Todri-Sanial
Nadine Azemard
Madeleine Abernot
Siegfried Karg
Elisabetta Corti
  • Function : Author
  • PersonId : 1067413
Kirsten Moselund
  • Function : Author
  • PersonId : 1067414
Juan Núñez
  • Function : Author
  • PersonId : 988882
Jamila Boudaden
  • Function : Author
Armin Klumpp
  • Function : Author
Théophile Gonos
  • Function : Author
  • PersonId : 1067415
Matthieu Rousseau
  • Function : Author
  • PersonId : 1067416
Tanguy Hardelin
  • Function : Author
  • PersonId : 1067417
Ahmed Nejim

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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Dates and versions

lirmm-02530086 , version 1 (03-04-2020)

Identifiers

  • HAL Id : lirmm-02530086 , version 1

Cite

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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