Training Hybrid Neural Networks with Multimode Optical Nonlinearities Using Digital Twins
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arXiv
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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866912188262776832 |
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| author | Oguz, Ilker Suter, Louis J. E. Hsieh, Jih-Liang Yildirim, Mustafa Dinc, Niyazi Ulas Moser, Christophe Psaltis, Demetri |
| author_facet | Oguz, Ilker Suter, Louis J. E. Hsieh, Jih-Liang Yildirim, Mustafa Dinc, Niyazi Ulas Moser, Christophe Psaltis, Demetri |
| contents | The ability to train ever-larger neural networks brings artificial intelligence to the forefront of scientific and technical discoveries. However, their exponentially increasing size creates a proportionally greater demand for energy and computational hardware. Incorporating complex physical events in networks as fixed, efficient computation modules can address this demand by decreasing the complexity of trainable layers. Here, we utilize ultrashort pulse propagation in multimode fibers, which perform large-scale nonlinear transformations, for this purpose. Training the hybrid architecture is achieved through a neural model that differentiably approximates the optical system. The training algorithm updates the neural simulator and backpropagates the error signal over this proxy to optimize layers preceding the optical one. Our experimental results achieve state-of-the-art image classification accuracies and simulation fidelity. Moreover, the framework demonstrates exceptional resilience to experimental drifts. By integrating low-energy physical systems into neural networks, this approach enables scalable, energy-efficient AI models with significantly reduced computational demands. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_07991 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Training Hybrid Neural Networks with Multimode Optical Nonlinearities Using Digital Twins Oguz, Ilker Suter, Louis J. E. Hsieh, Jih-Liang Yildirim, Mustafa Dinc, Niyazi Ulas Moser, Christophe Psaltis, Demetri Optics Artificial Intelligence The ability to train ever-larger neural networks brings artificial intelligence to the forefront of scientific and technical discoveries. However, their exponentially increasing size creates a proportionally greater demand for energy and computational hardware. Incorporating complex physical events in networks as fixed, efficient computation modules can address this demand by decreasing the complexity of trainable layers. Here, we utilize ultrashort pulse propagation in multimode fibers, which perform large-scale nonlinear transformations, for this purpose. Training the hybrid architecture is achieved through a neural model that differentiably approximates the optical system. The training algorithm updates the neural simulator and backpropagates the error signal over this proxy to optimize layers preceding the optical one. Our experimental results achieve state-of-the-art image classification accuracies and simulation fidelity. Moreover, the framework demonstrates exceptional resilience to experimental drifts. By integrating low-energy physical systems into neural networks, this approach enables scalable, energy-efficient AI models with significantly reduced computational demands. |
| title | Training Hybrid Neural Networks with Multimode Optical Nonlinearities Using Digital Twins |
| topic | Optics Artificial Intelligence |
| url | https://arxiv.org/abs/2501.07991 |