  We demonstrate the utility of machine learning algorithms for the design of
Oscillatory Neural Networks (ONNs). After constructing a circuit model of the
oscillators in a machine-learning-enabled simulator and performing
Backpropagation through time (BPTT) for determining the coupling resistances
between the ring oscillators, we show the design of associative memories and
multi-layered ONN classifiers. The machine-learning-designed ONNs show superior
performance compared to other design methods (such as Hebbian learning) and
they also enable significant simplifications in the circuit topology. We
demonstrate the design of multi-layered ONNs that show superior performance
compared to single-layer ones. We argue Machine learning can unlock the true
computing potential of ONNs hardware.
