Matrix computation, as a fundamental building block of information processing in science
and technology, contributes most of the computational overheads in modern signal …

Artificial intelligence tasks across numerous applications require accelerators for fast and
low-power execution. Optical computing systems may be able to meet these domain-specific …

The rapid development of information technology has led to urgent requirements for high
efficiency and ultralow power consumption. In the past few decades, neuromorphic …

Phase‐change materials (PCMs) are seeing tremendous interest for their use in
reconfigurable photonic devices; however, the most common PCMs exhibit a large …

Software implementations of brain-inspired computing underlie many important
computational tasks, from image processing to speech recognition, artificial intelligence and …

Optical neural networks (ONNs), or optical neuromorphic hardware accelerators, have the
potential to dramatically enhance the computing power and energy efficiency of mainstream …

Reservoir computing is a computational framework suited for temporal/sequential data
processing. It is derived from several recurrent neural network models, including echo state …

The simultaneous advances in artificial neural networks and photonic integration
technologies have spurred extensive research in optical computing and optical neural …

This tutorial–review on applications of artificial neural networks in photonics targets a broad
audience, ranging from optical research and engineering communities to computer science …

Neuromorphic computing has been attracting ever‐increasing attention due to superior
energy efficiency, with great promise to promote the next wave of artificial general …