A silicon photonic deep optical neural network integrating convolutional and fully connected layers with on-chip optoelectronic nonlinear activations operates with partially coherent light to achieve ...

Optical neural networks (ONNs) promise computing efficiency beyond microelectronics for modern artificial intelligence (AI). Current ONNs using analog matrix-vector multiplication (MVM) ...

Neural networks are one typical structure on which artificial intelligence can be based. The term neural describes their learning ability, which to some extent mimics the functioning of neurons in our ...

Research on ONNs began as early as the 1960s. To clearly illustrate the development history of ONNs, this review presents the evolution of related research work chronologically at the beginning of the ...

These multiple optical paths improve the system's ability to extract features from input data, offering a new way to optimize neural networks beyond just adding more layers or tweaking activation ...

In high-speed optical communications, traditional orbital angular momentum (OAM) multiplexing systems face fundamental limitations, including exponentially increasing spatial-domain complexity, ...

Researchers developed a fully integrated photonic processor that can perform all the key computations of a deep neural network on a photonic chip, using light. This advance could improve the speed and ...

“The ability to process and act on data in real time is increasingly critical for applications ranging from autonomous vehicles, three-dimensional environmental sensing, and remote robotics. However, ...

A technical paper titled “Training neural networks with end-to-end optical backpropagation” was published by researchers at University of Oxford and Lumai Ltd. “Optics is an exciting route for the ...

The idea of optical computing—the use of photons instead of electrons to perform computational operations—has been around for decades. However, interest has resurged in recent years; the potential for ...

Researchers in Sweden have developed a machine-learning approach that embeds the laws of physics directly into neural ...

The deep neural network models that power today's most demanding machine-learning applications have grown so large and complex that they are pushing the limits of traditional electronic computing ...