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

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 reshaping the field of information processing. With the
exponential growth of these DNNs challenging existing computing hardware, optical neural …

Research on optical computing has recently attracted significant attention due to the
transformative advances in machine learning. Among different approaches, diffractive …

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 …

Optical neural networks (ONNs), or optical neuromorphic hardware accelerators, have the
potential to dramatically enhance the computing power and energy efficiency of mainstream …