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Energy-based Learning of Complex Dynamical Systems

Obiettivo

As digital computing approaches its limits, a paradigm shift is needed to push the boundaries of computation. Neuromorphic computing, an analog computing paradigm inspired by the human brain, offers a promising alternative. Implementing mathematical models such as artificial neural networks directly in neuromorphic hardware holds tremendous potential: it can drastically reduce energy consumption while enabling faster inference when compared to digital architectures. Yet, a major bottleneck is the lack of dedicated algorithms for training analog circuits from data. Traditional methods such as backpropagation are unsuitable, as they require different circuits for training and inference, thus introducing energy-costly data transfer between circuits.

I propose a novel systems theory for learning in analog circuits, inspired by ideas from the literature on energy-based models, pioneered by Hopfield, Hinton and others. My approach is to cast the learning problem as the feedback interconnection of continuous-time circuit dynamics with an optimization algorithm. Using this framework, I will develop novel energy-based learning algorithms, capable of training circuits from input-output data. The developed algorithms are fully distributed, thus enabling efficient training of large-scale circuits, as well as robust to uncertain circuit elements, therefore accounting for device-to-device variations in neuromorphic hardware.

My preliminary results on resistive circuits already demonstrate the potential of this approach. Building on this foundation, the project will establish a comprehensive system-theoretic framework, capable of handling nonlinear and dynamic circuits with (mem)ristive and capacitive elements, as well as more general dissipative networks. This fundamental research project has the potential to significantly advance the theory of physics-based learning. The proposed theory will be supported by efficient computational tools and tested in neuromorphic hardware.

Campo scientifico (EuroSciVoc)

Parole chiave

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Meccanismo di finanziamento

HORIZON-ERC - HORIZON ERC Grants

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(si apre in una nuova finestra) ERC-2026-STG

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Istituzione ospitante

RIJKSUNIVERSITEIT GRONINGEN
Contributo netto dell'UE
€ 1 499 620,00
Costo totale
€ 1 499 620,00

Beneficiari (1)