EU H2020 NEURONN: Two-Dimensional Oscillatory Neural Networks for Energy Efficient Neuromorphic Computing
Abstract
Neuro-inspired computing employs technologies that enable brain-inspired computing hardware for more efficient and adaptive intelligent systems. Mimicking the human brain and nervous system, these computing architectures are excellent candidates for solving complex and large- scale associative learning problems.
The EU-funded NeurONN project will showcase a novel and alternative neuromorphic computing paradigm based on energy-efficient devices and architectures.
In the novel neuro-inspired computing architecture, information will be encoded in the phase of coupled oscillating neurons or oscillatory neural networks. The VO2 metal insulator transition devices will emulate biological neurons and are expected to be 250 times more efficient that the state-of-the-art digital CMOS based oscillators.
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