Two-Dimensional Oscillatory Neural Networks for Energy Efficient Neuromorphic Computing

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 proposed 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 state-of-the-art digital CMOS-based oscillators. Their 2D memristors that will emulate synapses are expected to be 330 times more efficient than the state-of-the-art.