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