Efficient data transport over multimode light-pipes with Megapixel images using differentiable ray tracing and Machine-learning

• URL: http://arxiv.org/abs/2301.06496v3
• Date: Thu, 24 Aug 2023 16:39:42 GMT
• Title: Efficient data transport over multimode light-pipes with Megapixel images using differentiable ray tracing and Machine-learning
• Authors: Joowon Lim, Jannes Gladrow, Douglas Kelly, Greg O'Shea, Govert Verkes, Ioan Stefanovici, Sebastian Nowozin, and Benn Thomsen
• Abstract summary: We demonstrate machine-learning-based decoding of large-scale digital images (pages) We use a millimeter-sized square cross-section waveguide to image an 8-bit spatial light modulator, presenting data as a matrix of symbols. By combining a digital twin of the setup with a U-Net, we can retrieve up to 66 kB using efficient convolutional operations only.
• Score: 6.677278996379261
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Retrieving images transmitted through multi-mode fibers is of growing interest, thanks to their ability to confine and transport light efficiently in a compact system. Here, we demonstrate machine-learning-based decoding of large-scale digital images (pages), maximizing page capacity for optical storage applications. Using a millimeter-sized square cross-section waveguide, we image an 8-bit spatial light modulator, presenting data as a matrix of symbols. Normally, decoders will incur a prohibitive O(n^2) computational scaling to decode n symbols in spatially scrambled data. However, by combining a digital twin of the setup with a U-Net, we can retrieve up to 66 kB using efficient convolutional operations only. We compare trainable ray-tracing-based with eigenmode-based twins and show the former to be superior thanks to its ability to overcome the simulation-to-experiment gap by adjusting to optical imperfections. We train the pipeline end-to-end using a differentiable mutual-information estimator based on the von-Mises distribution, generally applicable to phase-coding channels.

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