EU H2020 NEURONN: Two-Dimensional Oscillatory Neural Networks for Energy Efficient Neuromorphic Computing - LIRMM - Laboratoire d’Informatique, de Robotique et de Microélectronique de Montpellier Access content directly
Conference Papers Year : 2020

EU H2020 NEURONN: Two-Dimensional Oscillatory Neural Networks for Energy Efficient Neuromorphic Computing

Aida Todri-Sanial
Corentin Delacour
Madeleine Abernot
Nadine Azemard
Siegfried Karg
Elisabetta Corti
  • Function : Author
  • PersonId : 1067413
Juan Núñez
  • Function : Author
  • PersonId : 988882
Jafar Shamsi
  • Function : Author
  • PersonId : 1082869
Armin Klumpp
  • Function : Author
Jamila Boudaden
  • Function : Author
Théophile Gonos
  • Function : Author
  • PersonId : 1067415
Tanguy Hardelin
  • Function : Author
  • PersonId : 1067417
Marie Durieu
  • Function : Author
  • PersonId : 1082870
Ahmed Nejim
Slobodan Mijalkovic
  • Function : Author
  • PersonId : 1082871

Abstract

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 novel neuro-inspired computing architecture, information will be encoded in the phase of coupled oscillating neurons or oscillatory neural networks. The VO2 metal insulator transition devices will emulate biological neurons and are expected to be 250 times more efficient that the state-of-the-art digital CMOS based oscillators.
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Dates and versions

lirmm-03024126 , version 1 (25-11-2020)

Identifiers

  • HAL Id : lirmm-03024126 , version 1

Cite

Aida Todri-Sanial, Stefania Carapezzi, Corentin Delacour, Madeleine Abernot, Eirini Karachristou, et al.. EU H2020 NEURONN: Two-Dimensional Oscillatory Neural Networks for Energy Efficient Neuromorphic Computing. EFECS 2020 - European Forum for Electronic Components and Systems, Nov 2020, Brussels, Belgium. ⟨lirmm-03024126⟩
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