Photonic Neural Networks: A Compact Review
Mohammad Ahmadi, Hamidreza Bolhasani

TL;DR
This paper reviews the development of photonic neural networks, focusing on experimental, theoretical, and mathematical principles, highlighting recent advances, simulation methods, and key parameters in this emerging interdisciplinary field.
Contribution
It provides a comprehensive review of 18 key articles from 2015 to 2022, categorizing principles and emphasizing the role of mathematics and simulation in photonic neural networks.
Findings
Photonic neural networks leverage light for high-speed, low-precision computations.
Mathematical and simulation tools are crucial for advancing photonic neural network research.
Recent studies demonstrate significant progress in experimental and theoretical understanding.
Abstract
It has long been known that photonic science and especially photonic communications can raise the speed of technologies and producing manufacturing. More recently, photonic science has also been interested in its capabilities to implement low-precision linear operations, such as matrix multiplications, fast and effciently. For a long time most scientists taught that Electronics is the end of science but after many years and about 35 years ago had been understood that electronics do not answer alone and should have a new science. Today we face modern ways and instruments for doing tasks as soon as possible in proportion to many decays before. The velocity of progress in science is very fast. All our progress in science area is dependent on modern knowledge about new methods. In this research, we want to review the concept of a photonic neural network. For this research was selected 18…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsNeural Networks and Reservoir Computing · Semiconductor Lasers and Optical Devices · Optical Network Technologies
MethodsSPEED: Separable Pyramidal Pooling EncodEr-Decoder for Real-Time Monocular Depth Estimation on Low-Resource Settings
