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Artificial neural networks have become a staple computing technique in manyfields. Yet, they present fundamental differences with classical computinghardware in the way they process information. Photonic implementations ofneural network architectures potentially offer fundamental advantages overtheir electronic counterparts in terms of speed, processing parallelism,scalability and energy efficiency. Scalable and high performance photonicneural networks (PNNs) have been demonstrated, yet they remain scarce. In thiswork, we study the performance of such a scalable, fully parallel andautonomous PNN based on a large area vertical-cavity surface-emitting laser(LA-VCSEL). We show how the performance varies with different physicalparameters, namely, injection wavelength, injection power, and bias current.Furthermore, we link these physical parameters to the general computationalmeasures of consistency and dimensionality. We present a general method ofgauging dimensionality in high dimensional nonlinear systems subject to noise,which could be applied to many systems in the context of neuromorphiccomputing. Our work will inform future implementations of spatially multiplexedVCSEL PNNs.

Computational metrics and parameters of an injection-locked large area semiconductor laser for neural network computing