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

Research in photonic computing has flourished due to the proliferation of optoelectronic
components on photonic integration platforms. Photonic integrated circuits have enabled …

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

In optical communication systems, fibre nonlinearity is the major obstacle in increasing the
transmission capacity. Typically, digital signal processing techniques and hardware are …

With the proliferation of ultrahigh-speed mobile networks and internet-connected devices,
along with the rise of artificial intelligence (AI), the world is generating exponentially …

Electronically reprogrammable photonic circuits based on phase-change chalcogenides
present an avenue to resolve the von-Neumann bottleneck; however, implementation of …

Innovative approaches and tools play an important role in shaping design, characterization
and optimization for the field of photonics. As a subset of machine learning that learns …

Deep learning has become a widespread tool in both science and industry. However,
continued progress is hampered by the rapid growth in energy costs of ever-larger deep …

Neuromorphic photonics has recently emerged as a promising hardware accelerator, with
significant potential speed and energy advantages over digital electronics for machine …

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