Artificial neural networks (ANN) have become the backbone of modern artificial intelligence (AI) models that have already surpassed human performance in various tasks ranging from game playing to image recognition and text production. While ANNs are being successfully applied in a growing number of fields, their effective deployment in digital processors is hampered. The parallel processing nature of ANNs does not fit with the sequential nature of the Von Neumann architecture, requiring processors with high computational power and related energy consumption.

This problem has motivated the recent resurgence of analog computing, which addresses this inefficiency by physically implementing ANNs in the analog domain. In particular, photonic processors have demonstrated the potential to bring the high energy efficiency, low latency, and high bandwidth of optics to ANN processing. However, the field is still in its infancy, with significant knowledge gaps and a need for innovative solutions to fully exploit the capabilities of photonics in neural network applications.

This article collection aims to explore the latest advances in materials, architectures, and strategies towards the real-world implementation of photonic neuromorphic processors. The goal is to address the effective use of ANNs, or other non-conventional AI paradigms (such as reservoir computing), with photonics. By fostering interdisciplinary collaboration, this research seeks to unlock the full potential of photonics for computing, paving the way for faster and/or more energy-efficient computing paradigms.

We welcome the submission of Original Research, Review, Mini Review, and Perspective articles on themes including, but not limited to:

• Photonics neuromorphic computing

• Analog photonic processing

• Integration for photonic signal processing

• Photonic-electronic codesign and integration

• Programmable photonic signal processing

• Photonic reservoir computing

• Photonic Ising machines

• Optical spiking neurons and neural networks

• Application of neuromorphic photonic devices

• Machine learning models suited for photonics

• Ultrafast photonic processing

• Photonic quantum machine learning

• Photonic accelerated edge computing for networks of the future

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Frontiers in Nanotechnology

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