Ultrafast neuromorphic photonic image processing with a VCSEL neuron
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 (100 ps-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 potential 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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Mar 22, 2022
- Source ID
- 10.1038/s41598-022-08703-1
Entities
People
- Antonio Hurtado
- Gaetano Di Caterina
- Joshua Robertson
- Juan Arturo Alanis
- Julián Bueno
- Matěj Hejda
- Paul Kirkland
Organizations
- Engineering and Physical Sciences Research Council
- European Commission
- Office of Naval Research Global
- UK Research and Innovation