Optical High-Resolution Fully Convolutional Neural Network for Accelerated Image Classification

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Submitted: Nov 20, 2023
Final Revised: Apr 15, 2024
Accepted: May 15, 2024
Published: Jun 30, 2024
Abstract view: 11
PDF Download: 1
Volumes: Vol. 2 No. 1 (2024) Pages 31-46
DOI: 10.63208/21018-104

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L. Ming Hui, N. B. Khoa, and O. Adeyemi, “Optical High-Resolution Fully Convolutional Neural Network for Accelerated Image Classification”, CAI, vol. 2, no. 1, pp. 31–46, Jun. 2024, doi: 10.63208/21018-104.

Abstract

This study presents the development of FatNet, an optical fully convolutional neural network designed to transform traditional in silico architectures into a format optimized for high-resolution optical processing. By leveraging a free-space system, the network utilizes high-resolution feature maps and kernels without compromising frame rates. Unlike standard classifiers, FatNet integrates feature extraction and classification into a single fully convolutional framework, maximizing the parallelism of optical systems and reducing electronic-optical conversion overhead. Evaluation using the CIFAR100 dataset demonstrates that FatNet performs 8.2 times fewer convolution operations than ResNet-18 with only a marginal 6% reduction in accuracy. These results indicate that the optical implementation of FatNet achieves significantly faster inference than ResNet-18. This research provides a promising foundation for the advancement of deep learning models specifically tailored for the burgeoning era of optical computing through high-resolution kernel training.

Keywords: Optical Computing 4f System Fully Convolutional Neural Networks High-Resolution Kernels Inference Acceleration

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