Photonic Neuromorphic Accelerators for Event-Based Imaging Flow Cytometry
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arXiv
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| Format: | Preprint |
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2024
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| author | Tsilikas, Ioannis Tsirigotis, Aris Sarantoglou, George Deligiannidis, Stavros Bogris, Adonis Posch, Christoph Branden, Gerd Van den Mesaritakis, Charis |
| author_facet | Tsilikas, Ioannis Tsirigotis, Aris Sarantoglou, George Deligiannidis, Stavros Bogris, Adonis Posch, Christoph Branden, Gerd Van den Mesaritakis, Charis |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_10564 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Photonic Neuromorphic Accelerators for Event-Based Imaging Flow Cytometry Tsilikas, Ioannis Tsirigotis, Aris Sarantoglou, George Deligiannidis, Stavros Bogris, Adonis Posch, Christoph Branden, Gerd Van den Mesaritakis, Charis Optics Image and Video Processing 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. |
| title | Photonic Neuromorphic Accelerators for Event-Based Imaging Flow Cytometry |
| topic | Optics Image and Video Processing |
| url | https://arxiv.org/abs/2404.10564 |