Project description
Brain-like neural networks for energy-efficient AI solutions
The field of narrow AI is advancing, yet there is an increasing demand for more universal approaches to AI capable of operating across a wider range of applications. The EU-funded EXTRA-BRAIN project aims to develop flexible, hardware-friendly, and energy-efficient AI solutions based on brain-like neural networks. These networks aim to overcome the limitations of current AI methods, such as bounded reliability and excessive data dependence. The project leverages insights from computational neuroscience regarding the brain’s information processing principles, learning schemes, and neuroanatomical structures to design these networks. Supporting the models are data optimisation pipelines and an explainability framework. The project will validate this framework across various use cases with different hardware demands.
Objective
In parallel to the current developments in the so-called narrow artificial intelligence (AI) realm, there is an urgent demand for more universal, general AI approaches that can operate across a wider spectrum of application domains with varying data characteristics. It is expected that the emerging sustainable AI methods can be efficiently deployed in the edge-cloud continuum on different hardware platforms and computing infrastructure depending on the real-world task scenarios and constraints including the limited energy budget. In response to this growing demand and emerging trends we propose to adopt a brain-like approach to AI system design due to its promising potential for functional flexibility, hardware friendliness as well as energy efficiency among others. To this end, EXTRA-BRAIN is aimed at developing a new generation of AI solutions based on brain-like neural networks that enable us to overcome key limitations of the current state-of-the-art methods, exemplified by deep learning, such as limited cross-task generalisation and extrapolation to novel domains (bounded reliability), excessive dependence on costly annotated data as well as extensive training and validation processes with heavy demand for compute resources at high energy cost, to name a few. The core brain-like neural network design in our approach derives from the accumulated computational neuroscience insights into the brain's working principles of information processing, key learning schemes and neuroanatomical structures that underlie the brain's perceptual/cognitive phenomena and its functional flexibility. Furthermore, these novel models are supported by data optimisation pipelines, which improve data quality, security and reduce the costs of assembling suitable training data, and an explainability framework to empower the human user. The proposed EXTRA-BRAIN framework will be examined in a diverse set of use cases with different hardware demands in the edge-cloud continuum.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
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Keywords
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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HORIZON.2.4 - Digital, Industry and Space
MAIN PROGRAMME
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HORIZON.2.4.5 - Artificial Intelligence and Robotics
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Topic(s)
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Funding Scheme
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
HORIZON-RIA - HORIZON Research and Innovation Actions
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Call for proposal
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
(opens in new window) HORIZON-CL4-2023-HUMAN-01-CNECT
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Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.
100 44 Stockholm
Sweden
The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.