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Simulating an Integrated Photonic Image Classifier for Diffractive Neural Networks.

Huayi Sheng¹, Muhammad Shemyal Nisar¹

¹Sino-British College, University of Shanghai for Science and Technology, Shanghai 200093, China.

|January 23, 2024

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Integrated diffractive deep neural networks (ID2NNs) offer a promising solution for AI computing challenges. These all-optical networks leverage light's speed and parallelism for superior performance over electronic systems.

Area of Science:

Photonics
Artificial Intelligence
Computer Engineering

Background:

Moore's Law slowdown and the von Neumann bottleneck limit electronic computing for AI.
Conventional electronic neural networks struggle with the increasing demands of artificial intelligence.
Optical computing offers potential advantages in speed, parallelism, and power efficiency.

Purpose of the Study:

To present a detailed design framework for an integrated diffractive deep neural network (ID2NN).
To demonstrate the silicon-on-insulator (SOI) implementation of ID2NNs using Python-based simulations.
To evaluate the performance of the proposed ID2NN for image classification tasks.

Main Methods:

Development of a design framework for ID2NNs.
Simulation of ID2NNs on a silicon-on-insulator platform using Python.

Keywords:

computing metasurfaces diffractive neural networks integrated photonics photonic image classifier

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Performance evaluation using the MNIST dataset for image classification.

Main Results:

Successful design and simulation of an integrated diffractive deep neural network.
Demonstration of ID2NN functionality for matrix-vector operations crucial for neural networks.
Validation of the ID2NN's performance on image classification tasks.

Conclusions:

ID2NNs provide a viable, high-performance alternative for AI computing.
The proposed CMOS-compatible photonic approach enables efficient AI processor implementation.
Diffractive neural networks represent a significant advancement in optical computing for AI.