Abstract
We propose an original design for a neuron-inspired photonic computational primitive for a large-scale, ultrafast cognitive computing platform. The laser exhibits excitability and behaves analogously to a leaky integrate-and-fire (LIF) neuron. This model is both fast and scalable, operating up to a billion times faster than a biological equivalent and is realizable in a compact, vertical-cavity surface-emitting laser (VCSEL). We show that - under a certain set of conditions - the rate equations governing a laser with an embedded saturable absorber reduces to the behavior of LIF neurons. We simulate the laser using realistic rate equations governing a VCSEL cavity, and show behavior representative of cortical spiking algorithms simulated in small circuits of excitable lasers. Pairing this technology with ultrafast, neural learning algorithms would open up a new domain of processing.
Original language | English (US) |
---|---|
Article number | 6497478 |
Journal | IEEE Journal on Selected Topics in Quantum Electronics |
Volume | 19 |
Issue number | 5 |
DOIs | |
State | Published - 2013 |
All Science Journal Classification (ASJC) codes
- Atomic and Molecular Physics, and Optics
- Electrical and Electronic Engineering
Keywords
- Cognitive computing
- excitability
- leaky integrate-and-fire (LIF) neuron
- mixed-signal
- neural networks
- neuromorphic
- optoelectronics
- photonic neuron
- semiconductor lasers
- spike processing
- ultrafast
- vertical-cavity surface-emitting lasers (VCSELs)