Integrated photonic neural networks provide a promising platform for energy-efficient, high-
throughput machine learning with extensive scientific and commercial applications. Photonic …

Here we introduced an integrated photonics-based TPU by strategically utilizing a) photonic
parallelism via wavelength division multiplexing, b) high 2 Peta-operations-per second …

Convolutional neural networks (CNNs), inspired by biological visual cortex systems, are a
powerful category of artificial neural networks that can extract the hierarchical features of raw …

Photonic neural network implementation has been gaining considerable attention as a
potentially disruptive future technology. Demonstrating learning in large-scale neural …

Photonic computing, with potentials of high parallelism, low latency and high energy
efficiency, have gained progressive interest at the forefront of neural network (NN) …

The real-time massive-data intelligent information processing tasks highlight a vital
requirement for the novel, intelligent optimization hardware. Convolutional neural networks …

Neuromorphic photonics has relied so far either solely on coherent or Wavelength-Division-
Multiplexing (WDM) designs for enabling dot-product or vector-by-matrix multiplication …

Advancements in optical coherence control,,,–have unlocked many cutting-edge
applications, including long-haul communication, light detection and ranging (LiDAR) and …

Ultrafast dynamic machine vision in the optical domain can provide unprecedented
perspectives for high-performance computing. However, owing to the limited degrees of …

Driven by the remarkable breakthroughs during the past decade, photonics neural networks
have experienced a revival. Here, we provide a general overview of progress over the past …