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Photonic neuromorphic accelerators for event-based imaging flow cytometry.

I Tsilikas1,2, A Tsirigotis1, G Sarantoglou1

  • 1Department of Information and Communication Systems Engineering, University of the Aegean, Palama 2, 83100, Samos, Greece.

Scientific Reports
|October 15, 2024
PubMed
Summary
This summary is machine-generated.

This study merges event-based cameras with photonic neuromorphic processing for high-speed, label-free imaging cytometry. The bio-inspired system enhances classification accuracy and significantly reduces computational load for machine learning.

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Area of Science:

  • Biophotonics
  • Neuromorphic Engineering
  • Computational Imaging

Background:

  • High-speed imaging cytometry requires efficient data processing.
  • Traditional systems face challenges with high data rates and computational complexity.
  • Neuromorphic approaches offer bio-inspired solutions for event-driven data processing.

Purpose of the Study:

  • To develop and experimentally validate a high-speed, label-free imaging cytometry system.
  • To integrate an event-based camera with a photonic neuromorphic accelerator.
  • To assess the impact on classification accuracy and computational resource reduction.

Main Methods:

  • Utilized an event-based CMOS camera capturing 1 Gevents/sec.
  • Employed a photonic neuromorphic accelerator using passive optical spectrum slicing for analogue convolution.
  • Tested discrimination of artificial polymer beads flowing at 0.1 m/sec.

Main Results:

  • Achieved 98.2% classification accuracy with lightweight digital machine learning alone.
  • Attained 98.6% accuracy by pre-processing data with the photonic neuromorphic spectrum slicer at 3x10^6 images/sec.
  • Reduced trainable parameters in the digital back-end by a factor of 8-22.

Conclusions:

  • Demonstrated efficient merging of neuromorphic sensing and computing in a unified bio-inspired system.
  • Showcased holistic enhancement for emerging bio-imaging applications.
  • Validated the system's potential for high-throughput biological analysis.