Experimental Demonstration of Imperfection-Agnostic Local Learning Rules on Photonic Neural Networks with Mach-Zehnder Interferometric Meshes
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866909064486715392 |
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| author | Srouji, Luis El On, Mehmet Berkay Lee, Yun-Jhu Abdelghany, Mahmoud Yoo, S. J. Ben |
| author_facet | Srouji, Luis El On, Mehmet Berkay Lee, Yun-Jhu Abdelghany, Mahmoud Yoo, S. J. Ben |
| contents | Mach-Zehnder Interferometric meshes are attractive for low-loss photonic matrix multiplication but are challenging to program. Using least-squares optimization of directional derivatives, we experimentally demonstrate that desired matrix updates can be implemented agnostic to hardware imperfections. \c{opyright} 2024 The Author(s) |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_03564 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Experimental Demonstration of Imperfection-Agnostic Local Learning Rules on Photonic Neural Networks with Mach-Zehnder Interferometric Meshes Srouji, Luis El On, Mehmet Berkay Lee, Yun-Jhu Abdelghany, Mahmoud Yoo, S. J. Ben Optics Systems and Control Mach-Zehnder Interferometric meshes are attractive for low-loss photonic matrix multiplication but are challenging to program. Using least-squares optimization of directional derivatives, we experimentally demonstrate that desired matrix updates can be implemented agnostic to hardware imperfections. \c{opyright} 2024 The Author(s) |
| title | Experimental Demonstration of Imperfection-Agnostic Local Learning Rules on Photonic Neural Networks with Mach-Zehnder Interferometric Meshes |
| topic | Optics Systems and Control |
| url | https://arxiv.org/abs/2401.03564 |