A VCSEL based Photonic Neuromorphic Processor for Event-Based Imaging Flow Cytometry Applications

TL;DR

This paper introduces a VCSEL-based photonic neuromorphic processor integrated with event-based imaging for rapid, low-power classification of particles, achieving high accuracy with minimal memory and hardware demands.

Contribution

It presents a novel optical neuromorphic system combining VCSEL-based time-delayed learning with event-based imaging for efficient particle classification.

Findings

Achieved 95.8% classification accuracy.

Reduced memory requirements by up to 99.5%.

Lowered hardware needs by up to 84%.

Abstract

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