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

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

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) …

It has long been known that photonic communication can alleviate the data movement
bottlenecks that plague conventional microelectronic processors. More recently, there has …

With an ongoing trend in computing hardware toward increased heterogeneity, domain-
specific coprocessors are emerging as alternatives to centralized paradigms. The tensor …

In recent years, the explosive development of artificial intelligence implementing by artificial
neural networks (ANNs) creates inconceivable demands for computing hardware. However …

We demonstrate a novel interferometric coherent photonic crossbar architecture (Xbar) that
can realize any tensor operator and allows for total loss-induced fidelity restoration, offering …

Spiking neural networks (SNNs) offer an event-driven and more-biologically-realistic
alternative to standard artificial neural networks based on analog information processing …

The explosion of artificial intelligence and machine-learning algorithms, connected to the
exponential growth of the exchanged data, is driving a search for novel application-specific …

Convolutional Neural Networks (CNNs) are widely adopted in object recognition, speech
processing and machine translation, due to their extremely high inference accuracy …