We review different technologies and architectures for neuromorphic photonic accelerators,
spanning from bulk optics to photonic-integrated-circuits (PICs), and assess compute …

The relentless growth of Artificial Intelligence (AI) workloads has fueled the drive towards
non-Von Neuman architectures and custom computing hardware. Neuromorphic photonic …

Analog photonic computing comprises a promising candidate for accelerating the linear
operations of deep neural networks (DNNs), since it provides ultrahigh bandwidth, low …

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 …

The explosive volume growth of deep-learning (DL) applications has triggered an era in
computing, with neuromorphic photonic platforms promising to merge ultra-high speed and …

Reprogrammable optical meshes comprise a subject of heightened interest for the execution
of linear transformations, having a significant impact in numerous applications that extend …

Recently there has been growing interest in using photonics to perform the linear algebra
operations of neuromorphic and quantum computing applications, aiming at harnessing …

The recent explosive compute growth, mainly fueled by the boost of artificial intelligence (AI)
and deep neural networks (DNNs), is currently instigating the demand for a novel computing …

Non-von-Neumann computing architectures and deep learning training models have
sparked a new computational era where neurons are forming the main architectural …

The field of neuromorphic photonics has been projected to comprise the next-generation
Neural Network platform, expected to lead to remarkable advances in compute energy-and …