Skip to Main content Skip to Navigation
Conference papers

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

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.
Complete list of metadata

https://hal-lirmm.ccsd.cnrs.fr/lirmm-03024126
Contributor : Eirini Karachristou <>
Submitted on : Wednesday, November 25, 2020 - 4:56:06 PM
Last modification on : Saturday, December 26, 2020 - 1:46:07 PM
Long-term archiving on: : Friday, February 26, 2021 - 7:43:34 PM

File

EFECS-2020-Abstract.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : lirmm-03024126, version 1

Citation

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. European Forum for Electronic Components and Systems (EFECS), Nov 2020, Brussels, Belgium. ⟨lirmm-03024126⟩

Share

Metrics

Record views

127

Files downloads

40