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arXiv · 2307.08558

Nonlinear optical encoding enabled by recurrent linear scattering

Fei Xia·Kyungduk Kim·Yaniv Eliezer·SeungYun Han·Liam Shaughnessy·Sylvain Gigan·Hui Cao
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Abstract

Optical information processing and computing can potentially offer enhanced performance, scalability and energy efficiency. However, achieving nonlinearity-a critical component of computation-remains challenging in the optical domain. Here we introduce a design that leverages a multiple-scattering cavity to passively induce optical nonlinear random mapping with a continuous-wave laser at a low power. Each scattering event effectively mixes information from different areas of a spatial light modulator, resulting in a highly nonlinear mapping between the input data and output pattern. We demonstrate that our design retains vital information even when the readout dimensionality is reduced, thereby enabling optical data compression. This capability allows our optical platforms to offer efficient optical information processing solutions across applications. We demonstrate our design's efficacy across tasks, including classification, image reconstruction, keypoint detection and object detection, all of which are achieved through optical data compression combined with a digital decoder. In particular, high performance at extreme compression ratios is observed in real-time pedestrian detection. Our findings open pathways for novel algorithms and unconventional architectural designs for optical computing.

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physics.opticscond-mat.dis-nncs.ETcs.LGphysics.app-ph

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Fei Xia, Kyungduk Kim, Yaniv Eliezer, SeungYun Han, Liam Shaughnessy, Sylvain Gigan, Hui Cao. 2023-07-17. Nonlinear optical encoding enabled by recurrent linear scattering. https://doi.org/10.1038/s41566-024-01493-0

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