2D materials promise ultra-efficient neuromorphic computing
So-called neuromorphic computing uses technologies that imitate the human brain and nervous system. It is thus predestined to solve complex and comprehensive associative learning problems while offering the opportunity to significantly reduce the energy consumption of current silicon-based circuits.
In the proposed neuro-inspired computing architecture, information will be encoded in the phase of coupled oscillating neurons or oscillatory neural networks (ONN) interconnected to form a neural network. Just like the brain, the two key components in neuromorphic computing are called neuron and synapse – they replicate the distributed computing and memory units. The neurons used in the project are novel metal-insulator transition devices based on vanadium dioxide (VO2), which can be 250 times more efficient than state-of-the-art digital oscillators based on CMOS. The 2D memristors emulating the synapses are expected to be 330 times more efficient in terms of operating speed, lifetime and energy consumption than currently used technologies.
© Fraunhofer EMFT /Bernd Müller.
Over the next three years (1 January 2020 – 31 December 2022), the NeurONN project will bring together IBM Research Zurich, the Fraunhofer EMFT, CSIC/University of Seville, Silvaco, UK and AI Mergence, FR. It is coordinated by the French Centre National de la Recherche Scientifique (CNRS). Additionally, NeurONN has initiated an industrial advisory board including members from Intel Corporation and French startup Prophesee.
The project is funded under the EU research program Horizon 2020 with just over 4 million Euros.
NeurONN project – https://cordis.europa.eu/project/id/871501
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