Building Oscillatory Neural Networks

Abstract

This talk is about a novel computing paradigm based on coupled oscillatory neural networks. Oscillatory neural networks (ONNs) are recurrent neural networks where each neuron is an oscillator and oscillator couplings are the synaptic weights. Inspired by Hopfield Neural Networks, ONNs make use of nonlinear dynamics to compute and solve computational problems such as associative memory tasks and combinatorial optimization problems difficult to address with conventional digital computers. An exciting direction in recent years has been to implement Ising machines based on the Ising model of coupled binary spins on magnets. In this talk, I cover the design aspects of building ONNs from devices to architecture to allow to benefit from the parallel computations with oscillators while implementing them in an energy efficient way.