Convolutional Image Edge Detection Using Ultrafast Photonic Spiking VCSEL Neurons

TL;DR

This paper demonstrates ultrafast photonic neural networks using VCSELs for edge detection in images, achieving high-speed, brain-inspired processing compatible with optical communication systems.

Contribution

It introduces a novel photonic neuron model based on VCSELs for edge detection, combining ultrafast spiking responses with neural network principles.

Findings

VCSEL-based neurons detect image edges at sub-nanosecond speeds

Complete image edge detection achieved with gradient magnitude

System operates at telecom wavelength, suitable for optical data centers

Abstract

We report experimentally and in theory on the detection of edge information in digital images using ultrafast spiking optical artificial neurons towards convolutional neural networks (CNNs). In tandem with traditional convolution techniques, a photonic neuron model based on a Vertical-Cavity Surface Emitting Laser (VCSEL) is implemented experimentally to threshold and activate fast spiking responses upon the detection of target edge features in digital images. Edges of different directionalities are detected using individual kernel operators and complete image edge detection is achieved using gradient magnitude. Importantly, the neuromorphic (brain-like) image edge detection system of this work uses commercially sourced VCSELs exhibiting spiking responses at sub-nanosecond rates (many orders of magnitude faster than biological neurons) and operating at the telecom wavelength of 1300 nm; hence making our approach compatible with optical communication and data-center technologies. These results therefore have exciting prospects for ultrafast photonic implementations of neural networks towards computer vision and decision making systems for future artificial intelligence applications.