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Fat-U-Net: Non-Contracting U-Net for Free-Space Optical Neural Networks

Ibadulla, R., Reyes Aldasoro, C. C. ORCID: 0000-0002-9466-2018 & Chen, T. (2023). Fat-U-Net: Non-Contracting U-Net for Free-Space Optical Neural Networks. Paper presented at the AI and Optical Data Sciences V, 29 Jan - 1 Feb 2024, San Francisco, USA.

Abstract

This paper describes the advantages and disadvantages of adapting the U-Net architecture from a traditional GPU to a 4f free-space optical environment. The implementation is based on an optical-based acceleration called FatNet and thus this adaption is called Fat-U-Net. Fat-U-Net neglects the pooling operations in UNet, but maintains a similar number of weights and pixels per layer as U-Net. Our results demonstrate that the conversion to Fat-U-Net offers significant improvement in speed for segmentation tasks, with Fat-U-Net achieving a remarkable ×538 acceleration in inference compared to U-Net when both are run on optical devices and x37 acceleration in inference compared to the results provided by U-Net on GPU. The performance loss after conversion remains minimal in two datasets, with reductions of 4.24% in IoU for the Oxford IIIt pet dataset and 1.76% in IoU of HeLa cells nucleus segmentation.

Publication Type: Conference or Workshop Item (Paper)
Additional Information: Copyright the authors (2024) Society of Photo‑Optical Instrumentation Engineers (SPIE). One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this publication for a fee or for commercial purposes, and modification of the contents of the publication are prohibited.
Publisher Keywords: FatNet, HeLa segmentation, Optical Neural Network, segmentation
Subjects: R Medicine > RC Internal medicine > RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry
R Medicine > RE Ophthalmology
Departments: School of Science & Technology
School of Science & Technology > Computer Science
SWORD Depositor:
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