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THERMODON

Financiado

Thermodynamic-inspired computing with oscillatory neural networks

There is a pressing need to address the power consumption of computing, which keeps rising to the point it has become an environmental concern. Despite the remarkable progress in semiconductor technology, computing architectures a... ver más

30/04/2029

TU/e

2M€

Presupuesto del proyecto: 2M€

Líder del proyecto

TECHNISCHE UNIVERSITEIT EINDHOVEN 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 2024-02-13

ERC-2023-COG: ERC CONSOLIDATOR GRANTS Scope:Objectives

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Información adicional privada

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

1 Participantes

TU/e

2.00M€ | Lider

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Duración del proyecto: 62 meses Fecha Inicio: 2024-02-13
Fecha Fin: 2029-04-30

Líder del proyecto

TECHNISCHE UNIVERSITEIT EINDHOVEN No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 2M€

Descripción del proyecto There is a pressing need to address the power consumption of computing, which keeps rising to the point it has become an environmental concern. Despite the remarkable progress in semiconductor technology, computing architectures are still energy inefficient, engineered for deterministic tasks as well as susceptible to noise, heat, and variations. Instead of massively over-designing architectures to compute with an acceptable degree of reliability, this research aims to let physics do the computing and harness noise, heat and variabilities for energy efficient computing. At the heart of the proposed paradigm is the thermodynamics of open systems entwined with neuromorphic computing. THERMODON aims to develop an unconventional neuromorphic architecture to thermodynamically compute and self-organize (learn). I hypothesize that the natural thermodynamics of appropriately engineered architecture can harness noise, heat, and variations to self-organize toward energy efficient solutions to problems posed by external potentials. I will develop such architecture with neuromorphic oscillatory neural networks that I master in my lab. This research aims to address how thermodynamic principles can be applied to oscillatory neural networks to derive learning rules that are unsupervised, continuously adapting and transforming the architecture into a dynamic self-organizing and open interactive system that learns, infers and interacts with the environment. THERMODON will bring breakthrough innovations in thermodynamic computing models and AI-specialized hardware to enable online training and inference to intelligent systems. The interdisciplinary research in this project between neuromorphic computing and thermodynamics opens a new and exciting area in computer architecture, triggering a paradigm shift in edge AI computing as well as an immediate impact as a hardware accelerator platform.

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