  In this work, we present experimental results of a high-speed label-free
imaging cytometry system that seamlessly merges the high-capturing rate and
data sparsity of an event-based CMOS camera with lightweight photonic
neuromorphic processing. This combination offers high classification accuracy
and a massive reduction in the number of trainable parameters of the digital
machine-learning back-end. The photonic neuromorphic accelerator is based on a
hardware-friendly passive optical spectrum slicing technique that is able to
extract meaningful features from the generated spike-trains. The experimental
scenario comprises the discrimination of artificial polymethyl methacrylate
calibrated beads, having different diameters, flowing at a mean speed of
0.01m/sec. Classification accuracy, using only lightweight, digital
machine-learning schemes has topped at 98.2%. On the other hand, by
experimentally pre-processing the raw spike data through the proposed photonic
neuromorphic spectrum slicer we achieved an accuracy of 98.6%. This performance
was accompanied by a reduction in the number of trainable parameters at the
classification back-end by a factor ranging from 8 to 22, depending on the
configuration of the digital neural network.
