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Fun-COMP

Financiado

Cerrado

Functionally scaled computing technology From novel devices to non von Neumann...

Functionally scaled computing technology From novel devices to non von Neumann architectures and algorithms for a connected intelligent world The Fun-COMP project aims to develop a new wave of industry-relevant technologies that will extend the limits facing mainstream processing and storage approaches. We will do this by delivering innovative nanoelectronic and nanopho... ver más

31/08/2022

UNEXE

4M€

Presupuesto del proyecto: 4M€

Líder del proyecto

THE UNIVERSITY OF EXETER No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Fecha límite participación Sin fecha límite de participación.

Ver 7 Participantes

Financiación concedida El organismo H2020 notifico la concesión del proyecto el día 2022-08-31

ICT-31-2017: Micro- and nanoelectronics technolo...

I+D

Cerrada hace 7 años

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Información adicional privada

No hay información privada compartida para este proyecto. Habla con el coordinador.

7 Participantes

UNEXE

607.77K€ | Lider

UOXF

604.02K€ | Participante

THALES

588.55K€ | Participante

CNRS

413.30K€ | Participante

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Duración del proyecto: 57 meses Fecha Inicio: 2017-11-23
Fecha Fin: 2022-08-31

Líder del proyecto

THE UNIVERSITY OF EXETER No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 4M€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto The Fun-COMP project aims to develop a new wave of industry-relevant technologies that will extend the limits facing mainstream processing and storage approaches. We will do this by delivering innovative nanoelectronic and nanophotonic devices and systems that fuse together the core information processing tasks of computing and memory, that incorporate in hardware the ability to learn adapt and evolve, that are designed from the bottom-up to take advantage of the huge benefits, in terms of increases in speed/bandwidth and reduction in power consumption, promised by the emergence of Silicon photonic systems. We will develop basic information processing building blocks that draw inspiration from biological approaches, providing computing primitives that can mimic the essential features of brain-like synapses and neurons to deliver a new foundation for fast, low-power, functionally-scaled computing based around non-von Neumann approaches. We will combine such computing primitives into reconfigurable integrated processing networks that can implement in hardware novel, intelligent, self-learning and adaptive computational approaches - including spiking neural networks, computing-in-memory and autonomous reservoir computing – and that are capable of addressing complex real-world computational problems in fast, energy-efficient ways. We will address the application of our novel technologies to future computing imperatives, including the analysis and exploitation of ‘big data’ and the ubiquity of computing arising from the ‘Internet of Things’. To realise our goals we bring together a world-leading consortium of industrial and academic researchers whose current work in the development of future information processing and storage technologies defines the state-of-the-art.

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