Scalable intensity-based photonic matrix-vector multiplication processor using single-wavelength time-division-multiplexed signals
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866913861925339136 |
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| author | Chai, Chengli Tang, Rui Okano, Makoto Toprasertpong, Kasidit Takagi, Shinichi Takenaka, Mitsuru |
| author_facet | Chai, Chengli Tang, Rui Okano, Makoto Toprasertpong, Kasidit Takagi, Shinichi Takenaka, Mitsuru |
| contents | Photonic integrated circuits provide a compact platform for ultrafast and energy-efficient matrix-vector multiplications (MVMs) in the optical domain. Recently, schemes based on time-division multiplexing (TDM) have been proposed as scalable approaches for realizing large-scale photonic MVM processors. However, existing demonstrations rely on coherent detection or multiple wavelengths, both of which complicate their operations. In this work, we demonstrate a scalable TDM-based photonic MVM processor that uses only single-wavelength intensity-modulated optical signals, thereby avoiding coherent detection and enabling simplified operations. A 32-channel processor is fabricated on a Si-on-insulator (SOI) platform and used to experimentally perform convolution operations in a convolutional neural network (CNN) for handwritten digit recognition, achieving a classification accuracy of 93.47% for 1500 images. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_18194 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Scalable intensity-based photonic matrix-vector multiplication processor using single-wavelength time-division-multiplexed signals Chai, Chengli Tang, Rui Okano, Makoto Toprasertpong, Kasidit Takagi, Shinichi Takenaka, Mitsuru Optics Emerging Technologies Applied Physics Photonic integrated circuits provide a compact platform for ultrafast and energy-efficient matrix-vector multiplications (MVMs) in the optical domain. Recently, schemes based on time-division multiplexing (TDM) have been proposed as scalable approaches for realizing large-scale photonic MVM processors. However, existing demonstrations rely on coherent detection or multiple wavelengths, both of which complicate their operations. In this work, we demonstrate a scalable TDM-based photonic MVM processor that uses only single-wavelength intensity-modulated optical signals, thereby avoiding coherent detection and enabling simplified operations. A 32-channel processor is fabricated on a Si-on-insulator (SOI) platform and used to experimentally perform convolution operations in a convolutional neural network (CNN) for handwritten digit recognition, achieving a classification accuracy of 93.47% for 1500 images. |
| title | Scalable intensity-based photonic matrix-vector multiplication processor using single-wavelength time-division-multiplexed signals |
| topic | Optics Emerging Technologies Applied Physics |
| url | https://arxiv.org/abs/2501.18194 |