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NeurONN SIGNED

Two-Dimensional Oscillatory Neural Networks for Energy Efficient Neuromorphic Computing

Total Cost €

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EC-Contrib. €

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Partnership

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Project "NeurONN" data sheet

The following table provides information about the project.

Coordinator
CENTRE NATIONAL DE LA RECHERCHE SCIENTIFIQUE CNRS 

Organization address
address: RUE MICHEL ANGE 3
city: PARIS
postcode: 75794
website: www.cnrs.fr

contact info
title: n.a.
name: n.a.
surname: n.a.
function: n.a.
email: n.a.
telephone: n.a.
fax: n.a.

 Coordinator Country France [FR]
 Total cost 4˙232˙737 €
 EC max contribution 4˙232˙737 € (100%)
 Programme 1. H2020-EU.2.1.1. (INDUSTRIAL LEADERSHIP - Leadership in enabling and industrial technologies - Information and Communication Technologies (ICT))
 Code Call H2020-ICT-2019-2
 Funding Scheme RIA
 Starting year 2020
 Duration (year-month-day) from 2020-01-01   to  2022-12-31

 Partnership

Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    CENTRE NATIONAL DE LA RECHERCHE SCIENTIFIQUE CNRS FR (PARIS) coordinator 1˙064˙800.00
2    FRAUNHOFER GESELLSCHAFT ZUR FOERDERUNG DER ANGEWANDTEN FORSCHUNG E.V. DE (MUNCHEN) participant 1˙119˙975.00
3    IBM RESEARCH GMBH CH (RUESCHLIKON) participant 763˙687.00
4    AGENCIA ESTATAL CONSEJO SUPERIOR DEINVESTIGACIONES CIENTIFICAS ES (MADRID) participant 589˙440.00
5    A I MERGENCE FR (PARIS) participant 402˙085.00
6    SILVACO EUROPE LTD UK (ST IVES) participant 292˙750.00

Map

 Project objective

Neuro-inspired computing architectures are one of the leading candidates to solve complex and large-scale associative learning problems for AI applications. The two key building blocks for neuromorphic computing are the neuron and the synapse, which form the distributed computing and memory units. In the NeurONN project, we are proposing a novel neuro-inspired computing architecture where information is encoded in the “phase” of coupled oscillating neurons or oscillatory neural networks (ONN). Specifically, VO2 metal-insulator transition (MIT) devices and 2D memristors will be developed as neurons and synapses for hardware implementations. We predict VO2 MIT devices are up to 250X more energy efficient than state of the art digital CMOS based oscillators, where 2D memristors are up to 330X more energy efficient than state of the art TiO2 memristors. Moreover, the predicted energy efficiency gain of ONN architecture vs state of the art spiking neural network (SNN) architecture is up to 40X. Thus, NeurONN will showcase a novel and alternative energy efficient neuromorphic computing paradigm based on energy efficient devices and architectures. Such ONN will demonstrate synchronization and coupling dynamics for establishing collective learning behavior, in addition to desirable characteristics such as scaling, ultra-low power computation, and high computing performance. NeurONN aims to develop the first-ever ONN hardware platform (targeting two demonstrators) and complete with an ONN design methodology toolbox covering aspects from ONN architecture design to algorithms in order to facilitate adoption, testing and experimentation of ONN demonstrator chips by all potential users to unleash the potential of ONN technology.

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The information about "NEURONN" are provided by the European Opendata Portal: CORDIS opendata.

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