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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2405.05686 |
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| _version_ | 1866917812909375488 |
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| author | Mashiko, Ryosuke Naruse, Makoto Horisaki, Ryoichi |
| author_facet | Mashiko, Ryosuke Naruse, Makoto Horisaki, Ryoichi |
| contents | Optical computing is considered a promising solution for the growing demand for parallel computing in various cutting-edge fields, requiring high integration and high speed computational capacity. In this paper, we propose a novel optical computation architecture called diffraction casting (DC) for flexible and scalable parallel logic operations. In DC, a diffractive neural network (DNN) is designed for single instruction, multiple data (SIMD) operations. This approach allows for the alteration of logic operations simply by changing the illumination patterns. Furthermore, it eliminates the need for encoding and decoding the input and output, respectively, by introducing a buffer around the input area, facilitating end-to-end all-optical computing. We numerically demonstrate DC by performing all 16 logic operations on two arbitrary 256 bits parallel binary inputs. Additionally, we showcase several distinctive attributes inherent in DC, such as the benefit of cohesively designing the diffractive elements for SIMD logic operations, assuring high scalability and integration capability. Our study offers a novel design architecture for optical computers and paves the way for a next-generation optical computing paradigm. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_05686 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Diffraction casting Mashiko, Ryosuke Naruse, Makoto Horisaki, Ryoichi Optics Optical computing is considered a promising solution for the growing demand for parallel computing in various cutting-edge fields, requiring high integration and high speed computational capacity. In this paper, we propose a novel optical computation architecture called diffraction casting (DC) for flexible and scalable parallel logic operations. In DC, a diffractive neural network (DNN) is designed for single instruction, multiple data (SIMD) operations. This approach allows for the alteration of logic operations simply by changing the illumination patterns. Furthermore, it eliminates the need for encoding and decoding the input and output, respectively, by introducing a buffer around the input area, facilitating end-to-end all-optical computing. We numerically demonstrate DC by performing all 16 logic operations on two arbitrary 256 bits parallel binary inputs. Additionally, we showcase several distinctive attributes inherent in DC, such as the benefit of cohesively designing the diffractive elements for SIMD logic operations, assuring high scalability and integration capability. Our study offers a novel design architecture for optical computers and paves the way for a next-generation optical computing paradigm. |
| title | Diffraction casting |
| topic | Optics |
| url | https://arxiv.org/abs/2405.05686 |