EU H2020 NEURONN: Oscillatory neurons accelerate route to brain-like computing
Artificial Intelligence (AI) is expected to have a radical impact on the European economy. Today AI relies on high-performance hardware systems (central processing unit (CPUs) and graphic processing unit (GPUs), specialized AI accelerators, and high-performance networking equipment). But such hardware also consumes a lot of power and cannot cope with the ever-expanding AI workload and complexity. New thinking is needed to improve AI performance, lower the power consumption and match the needs of the variety of novel applications, which cannot simply exist with conventional hardware.
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. EU H2020 NEURONN contributes to this with its ultra-low-power capability, high energy-efficiency, and its CMOS compatible approach. EU H2020 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 than 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.
NEURONN is a European collaborative research project coordinated by CNRS-LIRMM, Dr. Aida Todri-Sanial, and consortium partners are IBM Research Zurich, Fraunhofer EMFT, CSIC, Silvaco, and AI Mergence.