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Synchronised neuro-Memristive Architecture for Reinforced learning Technology

Rapid progress in the regime of Artificial Intelligence and Internet-of-Things has enthused the development of fast and energy-efficient hardware to support future computing needs. One of the most prominent solutions is the deploy... ver más

31/08/2025

RIJKSUNIVERSITEIT...

203K€

Presupuesto del proyecto: 203K€

Líder del proyecto

RIJKSUNIVERSITEIT GRONINGEN No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Ver 2 Participantes

Financiación concedida El organismo HORIZON EUROPE notifico la concesión del proyecto el día 2022-06-20

Línea de financiación objetivo El proyecto se financió a través de la siguiente ayuda:

HORIZON-MSCA-2021-PF-01-01: MSCA Postdoctoral Fellowships 2021

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2 Participantes

RIJKSUNIVERSITEIT GRONINGEN

203.46K€ | Lider

IBM

N.D. | Participante

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Últimas noticias

29-11-2024: IDAE En las últimas 48 horas el Organismo IDAE ha otorgado 4 concesiones

29-11-2024: ECE En las últimas 48 horas el Organismo ECE ha otorgado 2 concesiones

29-11-2024: CMVAMADRID En las últimas 48 horas el Organismo CMVAMADRID ha otorgado 37 concesiones

Duración del proyecto: 38 meses Fecha Inicio: 2022-06-20
Fecha Fin: 2025-08-31

Líder del proyecto

RIJKSUNIVERSITEIT GRONINGEN

TRL 4-5

Presupuesto del proyecto 203K€

Descripción del proyecto Rapid progress in the regime of Artificial Intelligence and Internet-of-Things has enthused the development of fast and energy-efficient hardware to support future computing needs. One of the most prominent solutions is the deployment of cross-disciplinary resources for mimicking the performance of a Human Brain, also known as neuromorphic computing. It relies on electronic components that could replicate the functioning of neurons and synapses. Designing such novel electronics needs the development of cost-effective, fast and reliable materials with tunable functionality. A fundamental understanding of these materials will pave the foundation of innovative device designs and strategies for their large-scale integration for neuromorphic architectures. In this context, the proposed project intends to deliver artificial neurons by developing thin-films of a novel material (TbMnO3) that is capable of demonstrating a Negative Differential Resistance (NDR). The idea is to utilise the fundamental understanding of multi-dimensional (electrical, optical, mechanical and magnetic) control of NDR which is not explored yet and is possible in TbMnO3. NDR enables a two-terminal device to display self-oscillations due to applied electrical spikes. The spiking electrical currents could be used to govern the behaviour of these oscillations and emulating the leaky, integrate, and fire behaviour of biological neurons. Once such a performance is achieved, the device will further be integrated into oscillatory neural networks array for demonstration of complex computing tasks such as image recognition and imitating human behaviour. The project will unite the applicant’s expertise in synaptic devices and materials engineering with the extensive experience of the host-labs in thin-film and neuromorphic devices. Importantly, the project will warranty catapulting the applicant’s international recognition as an independent researcher and will improve his career prospects.

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