Abstract
In this paper, we showcase a leading-edge implementation of oscillatory neural networks (ONNs) using beyond Complementary-Metal-Oxide-Semiconductor devices based on vanadium dioxide to mimick neurons, and 2D molybdenum disulfide memristors to emulate synapses. We explore the ONN technology through simulations from materials to devices up to circuits. We show that ONNs naturally behave like associative memories and can be used for pattern recognition, a task to be exploited in edge devices. Finally, we develop a reconfigurable digital ONN-on-FPGA to assess ONN functionality in real world applications.