Multimodal deep learning plays a pivotal role in supporting the processing and learning of
diverse data types within the realm of artificial intelligence generated content (AIGC) …

Recent progress in artificial intelligence (AI) has boosted the computational possibilities in
fields in which standard computers are not able to perform adequately. The AI paradigm is to …

As a leading branch of deep learning, the convolutional neural network (CNN) is inspired by
the natural visual perceptron mechanism of living things, showing great application in image …

State-of-the-art deep learning models can converse and interact with humans by
understanding their emotions, but the exponential increase in model parameters has …

As a resonator-based optical hardware in analog optical computing, a microring synapse
can be straightforwardly configured to simulate the connection weights between neurons …

On-chip photonic neural networks (PNN) are emerging as an attractive solution for artificial
neural networks due to their high computing density, low energy consumption, and compact …

Photonic neural networks (PNNs) show tremendous potential for artificial intelligence
applications due to their higher computational rates than their traditional electronic …

We introduce a programmable eight-port interferometer with the recently proposed error-
tolerant architecture capable of performing a broad class of transformations. The …

Neuromorphic photonic computing has emerged as a competitive computing paradigm to
overcome the bottlenecks of the von-Neumann architecture. Linear weighting and nonlinear …

In recent years, integrated optical processing units (IOPUs) have demonstrated advantages
in energy efficiency and computational speed for neural network inference applications …