Ultrafast Neuromorphic Photonic Image Processing with a VCSEL Neuron

TL;DR

This paper presents a novel ultrafast all-optical neuromorphic image processor using a VCSEL neuron, capable of high-speed edge detection and complex image classification, advancing photonic AI hardware technology.

Contribution

It introduces a VCSEL-based photonic spike processor for optical image processing and demonstrates its integration with neural networks for classification tasks.

Findings

High-speed optical spike generation at 100 ps

Effective edge-feature detection in images

Successful integration with neural network for classification

Abstract

The ever-increasing demand for Artificial Intelligence (AI) systems is underlining a significant requirement for new, AI-optimised hardware. Neuromorphic (brain-like) processors are one highly-promising solution, with photonic-enabled realizations receiving increasing attention. Among these, approaches based upon Vertical Cavity Surface Emitting Lasers (VCSELs) are attracting interest given their favourable attributes and mature technology. Here, we demonstrate a hardware-friendly neuromorphic photonic spike processor, using a single VCSEL, for all-optical image edge-feature detection. This exploits the ability of a VCSEL-based photonic neuron to integrate temporally-encoded pixel data at high speed; and fire fast (100ps-long) optical spikes upon detecting desired image features. Furthermore, the photonic system is combined with a software-implemented spiking neural network yielding a full platform for complex image classification tasks. This work therefore highlights the potentials of VCSEL-based platforms for novel, ultrafast, all-optical neuromorphic processors interfacing with current computation and communication systems for use in future light-enabled AI and computer vision functionalities.