Photonic computing shows promise for transformative advancements in machine learning
(ML) acceleration, offering ultrafast speed, massive parallelism, and high energy efficiency …

The advent of coherent lasers has been a cornerstone of modern optics, with the prevailing
belief that greater coherence always confers an advantage. However, researchers from the …

Convolutional neural networks (CNNs), inspired by biological visual cortex systems, are a
powerful category of artificial neural networks that can extract the hierarchical features of raw …

A physics-based deep learning (DL) method termed Phynet is proposed for modeling the
nonlinear pulse propagation in optical fibers totally independent of the ground truth. The …

With the continuous miniaturization of conventional integrated circuits, obstacles such as
excessive cost, increased resistance to electronic motion, and increased energy …

With the swift advancement of neural networks and their expanding applications in many
fields, optical neural networks have gradually become a feasible alternative to electrical …

The technologically-relevant task of feature extraction from data performed in deep-learning
systems is routinely accomplished as repeated fast Fourier transforms (FFT) electronically in …

Photonic neural network implementation has been gaining considerable attention as a
potentially disruptive future technology. Demonstrating learning in large-scale neural …

If electro-optic conversion of current photonic NNs could be postponed until the very end of
the network, then the execution time is simply the photon time-of-flight delay. Here we …

Fast, sensitive, and compact devices that implement nonlinear activation functions are
needed to form fully connected photonic neural networks (PNNs). However, even in highly …