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

Integrated silicon photonics has sparked a significant ramp-up of investment in both
academia and industry as a scalable, power-efficient, and eco-friendly solution. At the heart …

The development of artificial intelligence is typically focused on computer algorithms and
integrated circuits. Recently, all-optical diffractive deep neural networks have been created …

There has been a resurgence of interest in optical computing since the early 2010s, both in
academia and in industry, with much of the excitement centred around special-purpose …

Recent years have witnessed promising artificial intelligence (AI) applications in many
disciplines, including optics, engineering, medicine, economics, and education. In particular …

Deep neural networks with applications from computer vision to medical diagnosis,,,–are
commonly implemented using clock-based processors,,,,,,,–, in which computation speed is …

Photonic computing enables faster and more energy-efficient processing of vision data,,,–.
However, experimental superiority of deployable systems remains a challenge because of …

Abstract In 1948, Dennis Gabor proposed the concept of holography, providing a pioneering
solution to a quantitative description of the optical wavefront. After 75 years of development …

In-sensor and near-sensor computing are becoming the next-generation computing
paradigm for high-density and low-power sensory processing. To fulfil a high-density and …

Optical imaging is commonly used for both scientific and technological applications across
industry and academia. In image sensing, a measurement, such as of an object's position or …