0_8301 Oscillatory neural networks (ONNs) are a promising neuromorphic computing paradigm that harnesses the nonlinear dynamics of physical systems based on a network of coupled oscillators.  ONNs are a promising neuromorphic computing paradigm for AI at the edge. ONNs [1-10] are networks of coupled oscillators using their natural synchronization behavior to compute. In the framework of the EU H2020 NeurONN project, we have explored ONN architectures, learning algorithms, and applications to showcase ONN advantages for edge computing. We developed a fully-connected ONN in digital to explore ONN edge applications and on-chip learning capabilities. Additionally, we have also investigated analog design ONN implementation and recently, we have designed and taped out a 16-oscillator node ONN in a 65nm technology node ASIC chip with fully connected topology for solving combinatorial optimization problems. In the framework of the Horizon EU PHASTRAC project, we are exploring novel devices such as phase change materials, VO₂ for oscillators and bilayer MO/HfO₂ ReRAM for synaptic devices that co-integration can enable a fully analog computing platform. An overview of the design and challenges based on novel materials, devices and circuit design implementation for ONNs will be presented. References:1.         M. Abernot, et al Two-Layered Oscillatory Neural Networks with Analog Feedforward Majority Gate for Image Edge Detection Application. ISCAS 2023  ⟨hal-04007951⟩2. C. Delacour, at al, A Mixed-Signal Oscillatory Neural Network for Scalable Analog Computations in Phase Domain. 2023. ⟨hal-03961010⟩3.  C. Delacour, et al "Energy-Performance Assessment of Oscillatory Neural Networks Based on VO₂ Devices for Future Edge AI Computing," doi: 10.1109/TNNLS.2023.3238473.
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