Scalability of On-chip Diffractive Optical Neural Networks
Fuente:
arXiv
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866914011516239872 |
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| author | Zarei, Sanaz |
| author_facet | Zarei, Sanaz |
| contents | This short report focuses on the scalability challenges of the on-chip diffractive optical neural networks. It addresses an emerging gap in the literature, specifically around the limitations and challenges of scaling optical neural networks on a chip. A thorough investigation of diffractive optical neural networks provides evidence that such networks are not capable of performing complex tasks and exhibit significant performance degradation as the number of classification categories increases. Despite optimizations, these networks classify only 3-4 classes, suggesting fundamental limitations in their computational scale. The inherent scalability challenges in these systems are underscored by the fact that the design parameters, such as the number of diffractive layers, the number of neurons per layer, and the inter-layer distances, cannot substantially change the performance. Therefore, the on-chip diffraction-based approach provides a limited number of controllable degrees of freedom compared to electronic neural networks, restricting the complexity of functions an on-chip diffractive neural network can learn. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_18493 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Scalability of On-chip Diffractive Optical Neural Networks Zarei, Sanaz Optics This short report focuses on the scalability challenges of the on-chip diffractive optical neural networks. It addresses an emerging gap in the literature, specifically around the limitations and challenges of scaling optical neural networks on a chip. A thorough investigation of diffractive optical neural networks provides evidence that such networks are not capable of performing complex tasks and exhibit significant performance degradation as the number of classification categories increases. Despite optimizations, these networks classify only 3-4 classes, suggesting fundamental limitations in their computational scale. The inherent scalability challenges in these systems are underscored by the fact that the design parameters, such as the number of diffractive layers, the number of neurons per layer, and the inter-layer distances, cannot substantially change the performance. Therefore, the on-chip diffraction-based approach provides a limited number of controllable degrees of freedom compared to electronic neural networks, restricting the complexity of functions an on-chip diffractive neural network can learn. |
| title | Scalability of On-chip Diffractive Optical Neural Networks |
| topic | Optics |
| url | https://arxiv.org/abs/2407.18493 |