  Spiking Neural Networks (SNNs) offer an event-driven and more biologically
realistic alternative to standard Artificial Neural Networks based on analog
information processing. This can potentially enable energy-efficient hardware
implementations of neuromorphic systems which emulate the functional units of
the brain, namely, neurons and synapses. Recent demonstrations of ultra-fast
photonic computing devices based on phase-change materials (PCMs) show promise
of addressing limitations of electrically driven neuromorphic systems. However,
scaling these standalone computing devices to a parallel in-memory computing
primitive is a challenge. In this work, we utilize the optical properties of
the PCM, Ge\textsubscript{2}Sb\textsubscript{2}Te\textsubscript{5} (GST), to
propose a Photonic Spiking Neural Network computing primitive, comprising of a
non-volatile synaptic array integrated seamlessly with previously explored
`integrate-and-fire' neurons. The proposed design realizes an `in-memory'
computing platform that leverages the inherent parallelism of
wavelength-division-multiplexing (WDM). We show that the proposed computing
platform can be used to emulate a SNN inferencing engine for image
classification tasks. The proposed design not only bridges the gap between
isolated computing devices and parallel large-scale implementation, but also
paves the way for ultra-fast computing and localized on-chip learning.
