Role of Spatial Coherence in Diffractive Optical Neural Networks
Fuente:
arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2023
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| _version_ | 1866917673832546304 |
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| author | Filipovich, Matthew J. Malyshev, Aleksei Lvovsky, A. I. |
| author_facet | Filipovich, Matthew J. Malyshev, Aleksei Lvovsky, A. I. |
| contents | Diffractive optical neural networks (DONNs) have emerged as a promising optical hardware platform for ultra-fast and energy-efficient signal processing for machine learning tasks, particularly in computer vision. Previous experimental demonstrations of DONNs have only been performed using coherent light. However, many real-world DONN applications require consideration of the spatial coherence properties of the optical signals. Here, we study the role of spatial coherence in DONN operation and performance. We propose a numerical approach to efficiently simulate DONNs under incoherent and partially coherent input illumination and discuss the corresponding computational complexity. As a demonstration, we train and evaluate simulated DONNs on the MNIST dataset of handwritten digits to process light with varying spatial coherence. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_03679 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Role of Spatial Coherence in Diffractive Optical Neural Networks Filipovich, Matthew J. Malyshev, Aleksei Lvovsky, A. I. Optics Image and Video Processing Diffractive optical neural networks (DONNs) have emerged as a promising optical hardware platform for ultra-fast and energy-efficient signal processing for machine learning tasks, particularly in computer vision. Previous experimental demonstrations of DONNs have only been performed using coherent light. However, many real-world DONN applications require consideration of the spatial coherence properties of the optical signals. Here, we study the role of spatial coherence in DONN operation and performance. We propose a numerical approach to efficiently simulate DONNs under incoherent and partially coherent input illumination and discuss the corresponding computational complexity. As a demonstration, we train and evaluate simulated DONNs on the MNIST dataset of handwritten digits to process light with varying spatial coherence. |
| title | Role of Spatial Coherence in Diffractive Optical Neural Networks |
| topic | Optics Image and Video Processing |
| url | https://arxiv.org/abs/2310.03679 |