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 …

Artificial intelligence has prevailed in all trades and professions due to the assistance of big
data resources, advanced algorithms, and high-performance electronic hardware. However …

The emergence of parallel convolution-operation technology has substantially powered the
complexity and functionality of optical neural networks (ONN) by harnessing the dimension …

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

Replacing electrons with photons is a compelling route toward high-speed, massively
parallel, and low-power artificial intelligence computing. Recently, diffractive networks …

New developments in hardware-based 'accelerators' range from electronic tensor cores and
memristor-based arrays to photonic implementations. The goal of these approaches is to …

Deep neural networks (DNNs) are resha** the field of information processing. With the
exponential growth of these DNNs challenging existing computing hardware, optical neural …

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) …

Convolutional neural networks are an important category of deep learning, currently facing
the limitations of electrical frequency and memory access time in massive data processing …