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ChipAI SIGNED

Energy-efficient and high-bandwidth neuromorphic nanophotonic Chips for Artificial Intelligence systems

Total Cost €

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EC-Contrib. €

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Partnership

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 ChipAI project word cloud

Explore the words cloud of the ChipAI project. It provides you a very rough idea of what is the project "ChipAI" about.

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Project "ChipAI" data sheet

The following table provides information about the project.

Coordinator
LABORATORIO IBERICO INTERNACIONAL DE NANOTECNOLOGIA 

Organization address
address: AVENIDA MESTRE JOSE VEIGA
city: BRAGA
postcode: 4715-330
website: www.inl.int

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 Portugal [PT]
 Total cost 3˙892˙005 €
 EC max contribution 3˙892˙005 € (100%)
 Programme 1. H2020-EU.1.2.1. (FET Open)
 Code Call H2020-FETOPEN-2018-2019-2020-01
 Funding Scheme RIA
 Starting year 2019
 Duration (year-month-day) from 2019-03-01   to  2022-02-28

 Partnership

Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    LABORATORIO IBERICO INTERNACIONAL DE NANOTECNOLOGIA PT (BRAGA) coordinator 653˙625.00
2    UNIVERSITY OF GLASGOW UK (GLASGOW) participant 660˙222.00
3    TECHNISCHE UNIVERSITEIT EINDHOVEN NL (EINDHOVEN) participant 604˙701.00
4    UNIVERSITY OF STRATHCLYDE UK (GLASGOW) participant 539˙925.00
5    IBM RESEARCH GMBH CH (RUESCHLIKON) participant 509˙156.00
6    IQE plc UK (Cardiff) participant 380˙000.00
7    FCIENCIAS.ID - ASSOCIACAO PARA A INVESTIGACAO E DESENVOLVIMENTO DE CIENCIAS PT (LISBON) participant 287˙500.00
8    UNIVERSITAT DE LES ILLES BALEARS ES (PALMA DE MALLORCA) participant 256˙875.00

Map

 Project objective

The same way the internet revolutionized our society, the rise of Artificial Intelligence (AI) that can learn without the need of explicit instructions is transforming our life. AI uses brain inspired neural network algorithms powered by computers. However, these central processing units (CPU) are extremely energy inefficient at implementing these tasks. This represents a major bottleneck for energy efficient, scalable and portable AI systems. Reducing the energy consumption of the massively dense interconnects in existing CPUs needed to emulate complex brain functions is a major challenge. ChipAI aims at developing a nanoscale photonics-enabled technology capable of deliver compact, high-bandwidth and energy efficiency CPUs using optically interconnected spiking neuron-like sources and detectors. ChipAI will pursue its main goal through the exploitation of Resonant Tunnelling (RT) semiconductor nanostructures embedded in sub-wavelength metal cavities, with dimensions 100 times smaller over conventional devices, for efficient light confinement, emission and detection. Key elements developed are non-linear RT nanoscale lasers, LEDs, detectors, and synaptic optical links on silicon substrates to make an economically viable technology. This platform will be able to fire and detect neuron-like light-spiking (pulsed) signals at rates 1 billion times faster than biological neurons (>10 GHz per spike rates) and requiring ultralow energy (<10 fJ). This radically new architecture will be tested for spike-encoding information processing towards validation for use in artificial neural networks. This will enable the development of real-time and offline portable AI and neuromorphic (brain-like) CPUs. In perspective, ChipAI will not only lay the foundations of the new field of neuromorphic optical computing, as will enable new non-AI functional applications in biosensing, imaging and many other fields where masses of cheap miniaturized pulsed sources and detectors are needed.

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

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