Prospects and applications of photonic neural networks

TL;DR

Photonic neural networks leverage optical physics to achieve high-speed, energy-efficient neuromorphic processing, promising advancements in AI, machine learning, and signal processing applications.

Contribution

This paper reviews the prospects and demonstrated applications of photonic neural networks, highlighting their potential for high-speed, low-energy neuromorphic computing.

Findings

Photonic neural networks enable sub-nanosecond latencies.

They offer high bandwidth and low energy consumption.

Demonstrated on integrated platforms and free-space optics.

Abstract

Neural networks have enabled applications in artificial intelligence through machine learning, and neuromorphic computing. Software implementations of neural networks on conventional computers that have separate memory and processor (and that operate sequentially) are limited in speed and energy efficiency. Neuromorphic engineering aims to build processors in which hardware mimics neurons and synapses in the brain for distributed and parallel processing. Neuromorphic engineering enabled by photonics (optical physics) can offer sub-nanosecond latencies and high bandwidth with low energies to extend the domain of artificial intelligence and neuromorphic computing applications to machine learning acceleration, nonlinear programming, intelligent signal processing, etc. Photonic neural networks have been demonstrated on integrated platforms and free-space optics depending on the class of applications being targeted. Here, we discuss the prospects and demonstrated applications of these photonic neural networks.