Neuro-inspired computing architectures are one of the leading candidates to solve complex and large-scale associative learning problems for AI applications. The two key building blocks for neuromorphic computing are the neuron and the synapse, which form the distributed computing and memory units. In the NeurONN project, we are proposing a novel neuro-inspired computing architecture where information is encoded in the “phase” of coupled oscillating neurons or oscillatory neural networks (ONN). Specifically, VO2 metal-insulator transition (MIT) devices and 2D memristors will be developed as neurons and synapses for hardware implementations. NeurONN will showcase a novel and alternative energy efficient neuromorphic computing paradigm based on energy efficient devices and architectures. Such ONN will demonstrate synchronization and coupling dynamics for establishing collective learning behaviour, in addition to desirable characteristics such as scaling, ultra-low power computation, and high computing performance. NeurONN aims to develop the first-ever ONN hardware platform (targeting two demonstrators) and an ONN design methodology toolbox covering aspects from ONN architecture design to algorithms.
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). With the end of CMOS scaling, it will be challenging and expensive using conventional hardware to cope with the ever-expanding AI workload and complexity. New thinking is needed to improve AI performance, lower the power consumption and match to the needs of the variety of novel applications, which cannot simply exist with conventional hardware. Both future edge and embedded devices may need some learning ability to support online learning while operating with low power consumption. NeurONN contributes to this with its ultra-low-powerr capability, high energy-efficiency and its CMOS compatible approach. The European industry will benefit from the ONN technology to enable online learning on the edge based on an energy efficient and alternative neuromorphic computing paradigm. Countless industry applications are interested in AI edge computing solutions in their products based on the use of NVIDIA chips on deep learning. Healthcare, Security & Surveillance, Robotics, Mobile, Devices and Gaming, Smart Sensors are some of the sectors that can benefit from the ONN technology.
The main objectives of NeurONN are:
(1) Demonstrate novel low-power computing devices based on metal-insulating-transition VO2 devices as the basis for emulating the oscillatory behavior of neurons.
(2) Demonstrate novel low-power nonvolatile resistive switches based on 2D nanomaterials for emulating the synaptic weights.
(3) Demonstrate proof-of-concept on unconventional computing paradigm based on oscillatory neural networks (ONN) in FPGAs and silicon CMOS.
(4) Develop the integration process for enabling ONN implementation based on MIT VO2 devices coupled with 2D memristors for energy efficient neuromorphic computing.
(5) Develop the atomistic to device-level modeling to understand the technology impact on process variation, interconnects, device uniformity and performance.
(6) Develop the advanced design methods covering aspects from devices, circuits to architecture-level for design-technology solutions for reliability, performance and energy-efficiency.
(7) Exploit the participation of academic and industrial partners from different scientific backgrounds to reinforce EU Nanoelectronics industry capability, innovate and sustain the technology integration requirements on beyond CMOS and also train the young generation of scientists in cutting-edge technologies.
