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

In the recent years, a dramatic boost of the research is observed at the junction of photonics,
machine learning and artificial intelligence. A new methodology can be applied to the …

Integrated circuits (ICs) and optoelectronic chips are the foundation stones of the modern
information society. The IC industry has been driven by the so-called “Moore's law” in the …

Recent advances in neuromorphic computing have established a computational framework
that removes the processor-memory bottleneck evident in traditional von Neumann …

Photonic neuromorphic computing has emerged as a promising approach to building a low-
latency and energy-efficient non-von Neuman computing system. A photonic spiking neural …

Photonics neuromorphic computing shows great prospects due to the advantages of low
latency, low power consumption and high bandwidth. Transistors with asymmetric electrode …

Learning and pattern recognition inevitably requires memory of previous events, a feature
that conventional CMOS hardware needs to artificially simulate. Dynamical systems …

In emerging artificial intelligence applications, massive matrix operations require high
computing speed and energy efficiency. Optical computing can realize high-speed parallel …

Nonlinear optical processes lie at the heart of frequency tunable coherent light sources,
including entangled photon sources and squeezed states of light, and they have become …

We proposed and experimentally demonstrated a simple and novel photonic spiking neuron
based on a distributed feedback (DFB) laser chip with an intracavity saturable absorber …