Neuromorphic machine vision in a network laser

We show that a lithographically designable network of waveguides etched into a wafer-bonded layer of InP can act as a physical neuromorphic computing system. The network is a lasing medium, with many spatially complex and overlapping modes competing for gain. The complex nonlinear interaction between these modes enables spectrally multiplexed feature detection in image data optically projected onto the network, with different spectral regions corresponding to different features. We use the network’s complex photonic dynamics to perform image classification tasks by training a single regression layer on the network’s spectral output, and construct a photonic convolutional neural network by combining the feature detection and classification layers, with 98.4% and 89.9% accuracies for MNIST and Fashion MNIST respectively. Finally, we explore how the graph properties of the network and tuning illumination parameters impact the machine learning performance of the system.