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MEMRINESS

Financiado

Memristive Neurons and Synapses for Neuromorphic Edge Computing

In recent years, Artificial Intelligence has shifted towards collaborative learning paradigms, where multiple systems acquire and elaborate data in real-time and share their experience to improve their performance. MEMRINESS will... ver más

30/04/2027

NAMLAB GGMBH

1M€

Presupuesto del proyecto: 1M€

Líder del proyecto

NAMLAB GGMBH No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Financiación concedida El organismo HORIZON EUROPE notifico la concesión del proyecto el día 2022-02-01

ERC-2021-STG: ERC STARTING GRANTS Scope:Objectives

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NAMLAB GGMBH

1.50M€ | Lider

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Duración del proyecto: 62 meses Fecha Inicio: 2022-02-01
Fecha Fin: 2027-04-30

Líder del proyecto

NAMLAB GGMBH No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 1M€

Descripción del proyecto In recent years, Artificial Intelligence has shifted towards collaborative learning paradigms, where multiple systems acquire and elaborate data in real-time and share their experience to improve their performance. MEMRINESS will generate new fundamental computing primitives that will overcome the current challenges for the deployment of intelligent systems on the edge. The requirements of a system operating on the edge are very tight: power efficiency, low area occupation, fast response times, and online learning. Brain-inspired architectures such as Spiking Neural Networks (SNNs) use artificial neurons and synapses that perform low-latency computation and internal-state storage simultaneously with very low power consumption, but at present they mainly rely on standard technologies, which make SNNs unfit to meet the above-mentioned constraints. Indeed, the dream of compact and efficient neurons and synapses, able to work at different time scales to match real-time constants and to retain memory of their state even in the absence of a power supply, cannot be realised without flanking standard technologies with emerging ones. In this respect, memristive technology has shown promising results, due to its ability to support non-volatile storage of the SNN parameters. Yet so far, research has prioritised the non-volatile properties of the devices rather than focusing additionally on the reproduction of multi-temporal synaptic and neural dynamics. To solve this problem, I will develop neurons and synapses that exploit the intrinsic physical characteristics and dynamics of volatile and non-volatile memristive devices to enable the design of compact, power efficient SNNs with multi timescale dynamics. I will use a holistic approach and co-develop every aspect, from the devices to the circuits, to the learning algorithms. I will use the results to design a SNN and demonstrate its collaborative and online learning capabilities in three scenarios of increasing complexity.

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