Fully multiplexed photonic tensor computing
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arXiv
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| Autori principali: | , , , , , , , , , , , , , , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| author | Sun, Aolong Zhao, Junhao Hu, Fangchen Xing, Sizhe Yuan, Yuqin He, Jialin Hu, Yongzhu Deng, Xuyu Liu, Yinjun Huang, Ouhan Zhao, Baiheng Liu, Hancheng Dong, Tian Zhou, Jingkai Sun, Haoyang Chen, Liang Shen, Chao Bao, Feng Li, Ziwei Shi, Jianyang Chu, Wei Dong, Bowei Chi, Nan Zhang, Junwen |
| author_facet | Sun, Aolong Zhao, Junhao Hu, Fangchen Xing, Sizhe Yuan, Yuqin He, Jialin Hu, Yongzhu Deng, Xuyu Liu, Yinjun Huang, Ouhan Zhao, Baiheng Liu, Hancheng Dong, Tian Zhou, Jingkai Sun, Haoyang Chen, Liang Shen, Chao Bao, Feng Li, Ziwei Shi, Jianyang Chu, Wei Dong, Bowei Chi, Nan Zhang, Junwen |
| contents | Tensor operations dominate modern computational workloads, yet their further acceleration demands hardware platforms with greater parallelism. Although photonic computing provides a compelling route for parallel processing, fully exploiting all native multiplexing dimensions of optical fields is impeded by the challenges in routing and programming light in all dimensions simultaneously. Here we introduce FieldCore, a fully multiplexed photonic tensor core that jointly harnesses wavelength, radio-frequency, guided-mode, time and space dimensions, thereby enabling parallelism to scale multiplicatively within a single optical field. Enabled by inverse-designed silicon photonics, FieldCore preserves a uniform programmed computation across all multiplexed channels in parallel. Experimentally, we validate and benchmark its performance from ultra-high-baudrate arithmetic operations to high-fidelity image convolution and parallel handwritten-digit recognition. We further use FieldCore to unlock applications that naturally require high-dimensional data processing, such as high-dimensional hyperspectral classification and massively parallel mechanical fault diagnosis. Our FieldCore supports an estimated aggregate compute throughput of 69.12 tera operations per second (TOPS) and accommodates up to 1,800 parallel input streams within a single core, establishing a scalable paradigm for fully multiplexed photonic tensor computing and AI inference. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_22660 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Fully multiplexed photonic tensor computing Sun, Aolong Zhao, Junhao Hu, Fangchen Xing, Sizhe Yuan, Yuqin He, Jialin Hu, Yongzhu Deng, Xuyu Liu, Yinjun Huang, Ouhan Zhao, Baiheng Liu, Hancheng Dong, Tian Zhou, Jingkai Sun, Haoyang Chen, Liang Shen, Chao Bao, Feng Li, Ziwei Shi, Jianyang Chu, Wei Dong, Bowei Chi, Nan Zhang, Junwen Optics Tensor operations dominate modern computational workloads, yet their further acceleration demands hardware platforms with greater parallelism. Although photonic computing provides a compelling route for parallel processing, fully exploiting all native multiplexing dimensions of optical fields is impeded by the challenges in routing and programming light in all dimensions simultaneously. Here we introduce FieldCore, a fully multiplexed photonic tensor core that jointly harnesses wavelength, radio-frequency, guided-mode, time and space dimensions, thereby enabling parallelism to scale multiplicatively within a single optical field. Enabled by inverse-designed silicon photonics, FieldCore preserves a uniform programmed computation across all multiplexed channels in parallel. Experimentally, we validate and benchmark its performance from ultra-high-baudrate arithmetic operations to high-fidelity image convolution and parallel handwritten-digit recognition. We further use FieldCore to unlock applications that naturally require high-dimensional data processing, such as high-dimensional hyperspectral classification and massively parallel mechanical fault diagnosis. Our FieldCore supports an estimated aggregate compute throughput of 69.12 tera operations per second (TOPS) and accommodates up to 1,800 parallel input streams within a single core, establishing a scalable paradigm for fully multiplexed photonic tensor computing and AI inference. |
| title | Fully multiplexed photonic tensor computing |
| topic | Optics |
| url | https://arxiv.org/abs/2604.22660 |