A VCSEL based Photonic Neuromorphic Processor for Event-Based Imaging Flow Cytometry Applications
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866913844750712832 |
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| author | Skontranis, M. Moustakas, G. Bogris, A. Mesaritakis, C. |
| author_facet | Skontranis, M. Moustakas, G. Bogris, A. Mesaritakis, C. |
| contents | This work presents an optical neuromorphic imaging and processing cytometry system that integrates an excitable VCSEL-based time-delayed (TD) extreme learning machine with an event-based 2D camera. The proposed system is designed for the classification of Polymethyl Methacrylate (PMMA) particles of varying diameters moving at speeds between 0.01 and 0.1 m/s. The TD photonic scheme achieved a classification accuracy of 95.8% while encoding the original 2D images into a 1-bit spike stream containing a maximum of 96 spikes. Additionally, the binary representation of the synthetic frames enables a significant reduction in memory and hardware requirements, ranging from 98.4% to 99.5% and 50% to 84%, respectively. These findings demonstrate that the integration of neuromorphic computing with sensing can facilitate the development of low-power, low-latency applications optimized for resource-constrained environments |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_12026 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A VCSEL based Photonic Neuromorphic Processor for Event-Based Imaging Flow Cytometry Applications Skontranis, M. Moustakas, G. Bogris, A. Mesaritakis, C. Optics Image and Video Processing This work presents an optical neuromorphic imaging and processing cytometry system that integrates an excitable VCSEL-based time-delayed (TD) extreme learning machine with an event-based 2D camera. The proposed system is designed for the classification of Polymethyl Methacrylate (PMMA) particles of varying diameters moving at speeds between 0.01 and 0.1 m/s. The TD photonic scheme achieved a classification accuracy of 95.8% while encoding the original 2D images into a 1-bit spike stream containing a maximum of 96 spikes. Additionally, the binary representation of the synthetic frames enables a significant reduction in memory and hardware requirements, ranging from 98.4% to 99.5% and 50% to 84%, respectively. These findings demonstrate that the integration of neuromorphic computing with sensing can facilitate the development of low-power, low-latency applications optimized for resource-constrained environments |
| title | A VCSEL based Photonic Neuromorphic Processor for Event-Based Imaging Flow Cytometry Applications |
| topic | Optics Image and Video Processing |
| url | https://arxiv.org/abs/2505.12026 |