Photonics for artificial intelligence and neuromorphic computing

TL;DR

This paper reviews recent advances in photonic technologies for artificial intelligence and neuromorphic computing, highlighting their potential for ultrafast processing and addressing latency challenges in AI systems.

Contribution

It provides a comprehensive overview of integrated photonic neuromorphic systems, discussing recent progress, challenges, and future technological needs.

Findings

Photonic integration enables ultrafast neural networks.

Neuromorphic photonics offers sub-nanosecond latencies.

Advances are needed in science and technology for future development.

Abstract

Research in photonic computing has flourished due to the proliferation of optoelectronic components on photonic integration platforms. Photonic integrated circuits have enabled ultrafast artificial neural networks, providing a framework for a new class of information processing machines. Algorithms running on such hardware have the potential to address the growing demand for machine learning and artificial intelligence, in areas such as medical diagnosis, telecommunications, and high-performance and scientific computing. In parallel, the development of neuromorphic electronics has highlighted challenges in that domain, in particular, related to processor latency. Neuromorphic photonics offers sub-nanosecond latencies, providing a complementary opportunity to extend the domain of artificial intelligence. Here, we review recent advances in integrated photonic neuromorphic systems, discuss current and future challenges, and outline the advances in science and technology needed to meet those challenges.