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MANOLO

Financiado

Cerrado

Trustworthy Efficient AI for Cloud-Edge Computing

MANOLO will deliver a complete stack of trustworthy algorithms and tools to help AI systems reach better efficiency and seamless optimization in their operations, resources and data required to train, deploy and run high-quality a... ver más

31/12/2026

NUID UCD

9M€

Presupuesto del proyecto: 9M€

Líder del proyecto

UNIVERSITY COLLEGE DUBLIN NATIONAL UNIVERSITY... No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Fecha límite participación Sin fecha límite de participación.

Financiación concedida El organismo HORIZON EUROPE notifico la concesión del proyecto el día 2024-01-01

HORIZON-CL4-2023-HUMAN-01-04: Open innovation: Addressing Grand c... ExpectedOutcome:Projects are expected to contribute to the following outcomes:

Innovación

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Características del participante

Este proyecto no cuenta con búsquedas de partenariado abiertas en este momento.

Información adicional privada

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

Participantes

NUID UCD

Lider

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Duración del proyecto: 35 meses Fecha Inicio: 2024-01-01
Fecha Fin: 2026-12-31

Líder del proyecto

UNIVERSITY COLLEGE DUBLIN NATIONAL UNIVERSITY... No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 9M€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto MANOLO will deliver a complete stack of trustworthy algorithms and tools to help AI systems reach better efficiency and seamless optimization in their operations, resources and data required to train, deploy and run high-quality and lighter AI models in both centralised and cloud-edge distributed environments. It will push the state of the art in the development of a collection of complementary algorithms for training, understanding, compressing and optimising machine learning models by advancing research in the areas of: model compression, meta-learning (few-shot learning), domain adaptation, frugal neural network search and growth and neuromorphic models. Novel dynamic algorithms for data/energy efficient and policy-compliance allocation of AI tasks to assets and resources in the cloud-edge continuum will be designed, allowing for trustworthy widespread deployment. To support these activities a data management framework for distributed tracking of assets and their provenance (data, models, algorithms) and a benchmark system to monitor, evaluate and compare new AI algorithms and model deployments will be developed. Trustworthiness evaluation mechanisms will be embedded at its core for explainability, robustness and security of models while using the Z-Inspection methodology for TrustworthyAI assesment, helping AI systems conform to the new AI Act regulation. MANOLO will be deployed as a toolset and tested in lab environments via Use Cases with different distributed AI paradigms within cloud-edge continuum settings; it will be validated in verticals such as health, manufacturing, and telecommunications aligned with ADRA identified market opportunities, and with a granular set of embedded devices covering robotics, smartphones, IoT as well as using Neuromorphic chips. MANOLO will integrate with ongoing projects at EU level developing the next operating system for cloud-edge continuum, while promoting its sustainability via the AI-on-demand platform and EU portals.

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