Neuromorphic encoding of image pixel data into rate-coded optical spike trains with a photonic VCSEL-neuron
Abstract
Driven by the increasing significance of artificial intelligence, the field of neuromorphic (brain-inspired) photonics is attracting increasing interest, promising new, high-speed, and energy-efficient computing hardware for key applications in information processing and computer vision. Widely available photonic devices, such as vertical-cavity surface emitting lasers (VCSELs), offer highly desirable properties for photonic implementations of neuromorphic systems, such as high-speed and low energy operation, neuron-like dynamical responses, and ease of integration into chip-scale systems. Here, we experimentally demonstrate encoding of digital image data into continuous, rate-coded, up to GHz-speed optical spike trains with a VCSEL-based photonic spiking neuron. Moreover, our solution makes use of off-the-shelf fiber-optic components with operation at telecom wavelengths, therefore making the system compatible with current optical network and data center technologies. This VCSEL-based spiking encoder paves the way toward optical spike-based data processing and ultrafast neuromorphic vision systems.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Jun 01, 2021
- Source ID
- 10.1063/5.0048674
Entities
People
- Antonio Hurtado
- Joshua Robertson
- Juan Arturo Alanis
- Julián Bueno
- Matěj Hejda
Organizations
- Engineering and Physical Sciences Research Council
- European Commission
- Office of Naval Research Global
- UK Research and Innovation
- University of Strathclyde