SOURCE-LINKED INTELLIGENCE

Meta-Learning of memristive devices for lifelong adaptation

CORDIS · observation · Publication date unknown

Meta-Learning of memristive devices for lifelong adaptation 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 t

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recordType

award

status

SIGNED

region

EU

value

207108.8

unit

EUR