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arXiv · 2404.10564

Photonic Neuromorphic Accelerators for Event-Based Imaging Flow Cytometry

Ioannis Tsilikas·Aris Tsirigotis·George Sarantoglou·Stavros Deligiannidis·Adonis Bogris·Christoph Posch·Gerd Van den Branden·Charis Mesaritakis
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Abstract

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.

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Ioannis Tsilikas, Aris Tsirigotis, George Sarantoglou, Stavros Deligiannidis, Adonis Bogris, Christoph Posch, Gerd Van den Branden, Charis Mesaritakis. 2024-04-16. Photonic Neuromorphic Accelerators for Event-Based Imaging Flow Cytometry. https://doi.org/10.1038/s41598-024-75667-9

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