We provide an overview of the rapidly evolving landscape of integrated photonic
neuromorphic architectures, specifically targeting the implementation of convolutional neural …

Photonic technologies have shown a promising way to build high-speed and high-energy-
efficiency neural network accelerators. In previously presented photonic neural networks …

Deep learning (DL) has achieved state-of-the-art performance in many challenging
problems. However, DL requires powerful hardware for both training and deployment …

In recent years, there has been a significant focus on achieving low-latency and high-
throughput convolutional neural network (CNN) inference. Integrated photonics offers the …

Recent success in deep neural networks has generated strong interest in hardware
accelerators to improve speed and energy consumption. This paper presents a new type of …

Photonic neural network accelerators (PNNAs) have been lately brought into the spotlight as
a new class of custom hardware that can leverage the maturity of photonic integration …

Neuromorphic computing has emerged as a highly-promising compute alternative, migrating
from von-Neuman architectures towards mimicking the human brain for sustaining …

Ever‐growing demands of bandwidth, computing speed, and power consumption are now
accelerating the transformation of computing research, as work‐at‐home becomes a new …

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

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 …