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Meta-Learning of memristive devices for lifelong adaptation

Project description

Solution to improve AI sustainability

Advancements in AI technologies, coupled with the widespread adoption of machine learning (ML) applications, have led to an increase in cloud usage and global energy consumption. In response, developers are shifting towards deploying smart devices at the edge of networks. Supported by the Marie Skłodowska-Curie Actions programme, the MALEFICENT project aims to enhance sustainability and reduce energy costs by developing a novel framework for implementing and using sustainable AI applications at the edge. The project will focus on creating a self-adaptive neuromorphic system that leverages emerging memory devices, addressing their limitations while enhancing energy efficiency and non-volatility. This approach is an alternative to current AI systems. The project’s findings are expected to provide insights into emerging memory technologies.

Objective

The continued growth of artificial intelligence (AI) in the cloud is driving up global energy costs. As a result, a paradigm shift is taking place where new intelligent devices are placed right at the edge. MALEFICENT will create a new framework for implementing sustainable AI at the edge using standard and novel technologies. Neuromorphic systems using emerging memory devices such as resistive switching devices (ReRAM) or ferroelectric capacitors (FeCap), are a promising alternative for AI systems thanks to their energy efficiency and non-volatility. However, the deployment of these devices in real-world applications poses some challenges, due to their intrinsic variability and limited bit precision. I will use advanced learning techniques such as meta-learning to create a self-adaptive neuromorphic system based on emerging memory devices able to exploit the intrinsic features of the devices while mitigating their limitations. I will then apply it to a real-world environment, such as robotics. This will have a significant impact on the research of emerging memory technologies, by opening up the possibility of exploitation in an industrial context.

Fields of science (EuroSciVoc)

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Keywords

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Programme(s)

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Topic(s)

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Funding Scheme

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HORIZON-TMA-MSCA-PF-EF - HORIZON TMA MSCA Postdoctoral Fellowships - European Fellowships

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Call for proposal

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(opens in new window) HORIZON-MSCA-2024-PF-01

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Coordinator

RIJKSUNIVERSITEIT GRONINGEN
Net EU contribution

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.

€ 217 076,16
Address
Broerstraat 5
9712CP Groningen
Netherlands

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Activity type
Higher or Secondary Education Establishments
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Total cost

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Partners (2)

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