Simulating an Integrated Photonic Image Classifier for Diffractive Neural Networks

The slowdown of Moore's law and the existence of the "von Neumann bottleneck" has led to electronic-based computing systems under von Neumann's architecture being unable to meet the fast-growing demand for artificial intelligence computing. However, all-optical diffractive neural...

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Bibliographic Details
Published in:Micromachines (Basel) 2023-12, Vol.15 (1), p.50
Main Authors: Sheng, Huayi, Nisar, Muhammad Shemyal
Format: Article
Language:English
Subjects:
Artificial intelligence
Artificial neural networks
Back propagation
Classification
Computation
Computational linguistics
computing metasurfaces
Datasets
Design
diffractive neural networks
Image classification
integrated photonics
Language processing
Mathematical analysis
Microprocessors
Natural language interfaces
Neural networks
Neurons
Optical communication
photonic image classifier
Photonics
Power consumption
Propagation
Silicon
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Summary:The slowdown of Moore's law and the existence of the "von Neumann bottleneck" has led to electronic-based computing systems under von Neumann's architecture being unable to meet the fast-growing demand for artificial intelligence computing. However, all-optical diffractive neural networks provide a possible solution to this challenge. They can outperform conventional silicon-based electronic neural networks due to the significantly higher speed of the propagation of optical signals (≈108 m.s-1) compared to electrical signals (≈105 m.s-1), their parallelism in nature, and their low power consumption. The integrated diffractive deep neural network (ID2NN) uses an on-chip fully passive photonic approach to achieve the functionality of neural networks (matrix-vector operations) and can be fabricated via the CMOS process, which is technologically more amenable to implementing an artificial intelligence processor. In this paper, we present a detailed design framework for the integrated diffractive deep neural network and corresponding silicon-on-insulator integration implementation through Python-based simulations. The performance of our proposed ID2NN was evaluated by solving image classification problems using the MNIST dataset.
ISSN:2072-666X
2072-666X
DOI:10.3390/mi15010050