Photonic Neural Networks: A Compact Review
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 multiplicatio...
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Zusammenfassung: | 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 main articles were among the
main 30 articles on this subject from 2015 to the 2022 year. These articles
noticed three principles: 1- Experimental concepts, 2- Theoretical concepts,
and, finally 3- Mathematic concepts. We should be careful with this research
because mathematics has a very important and constructive role in our topics!
One of the topics that are very valid and also new, is simulation. We used to
work with simulation in some parts of this research. First, briefly, we start
by introducing photonics and neural networks. In the second we explain the
advantages and disadvantages of a combination of both in the science world and
industries and technologies about them. Also, we are talking about the
achievements of a thin modern science. Third, we try to introduce some
important and valid parameters in neural networks. In this manner, we use many
mathematic tools in some portions of this article. |
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DOI: | 10.48550/arxiv.2302.08390 |