Harnessing Photonics for Machine Intelligence
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866914467407724544 |
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| author | Zhu, Hanqing Ning, Shupeng Zhou, Hongjian Yin, Ziang Chen, Ray T. Gu, Jiaqi Pan, David Z. |
| author_facet | Zhu, Hanqing Ning, Shupeng Zhou, Hongjian Yin, Ziang Chen, Ray T. Gu, Jiaqi Pan, David Z. |
| contents | The exponential growth of machine-intelligence workloads is colliding with the power, memory, and interconnect limits of the post-Moore era, motivating compute substrates that scale beyond transistor density alone. Integrated photonics is emerging as a candidate for artificial intelligence (AI) acceleration by exploiting optical bandwidth and parallelism to reshape data movement and computation. This review reframes photonic computing from a circuits-and-systems perspective, moving beyond building-block progress toward cross-layer system analysis and full-stack design automation. We synthesize recent advances through a bottleneck-driven taxonomy that delineates the operating regimes and scaling trends where photonics can deliver end-to-end sustained benefits. A central theme is cross-layer co-design and workload-adaptive programmability to sustain high efficiency and versatility across evolving application domains at scale. We further argue that Electronic-Photonic Design Automation (EPDA) will be pivotal, enabling closed-loop co-optimization across simulation, inverse design, system modeling, and physical implementation. By charting a roadmap from laboratory prototypes to scalable, reproducible electronic-photonic ecosystems, this review aims to guide the CAS community toward an automated, system-centric era of photonic machine intelligence. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_10841 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Harnessing Photonics for Machine Intelligence Zhu, Hanqing Ning, Shupeng Zhou, Hongjian Yin, Ziang Chen, Ray T. Gu, Jiaqi Pan, David Z. Optics Artificial Intelligence Hardware Architecture Emerging Technologies Machine Learning The exponential growth of machine-intelligence workloads is colliding with the power, memory, and interconnect limits of the post-Moore era, motivating compute substrates that scale beyond transistor density alone. Integrated photonics is emerging as a candidate for artificial intelligence (AI) acceleration by exploiting optical bandwidth and parallelism to reshape data movement and computation. This review reframes photonic computing from a circuits-and-systems perspective, moving beyond building-block progress toward cross-layer system analysis and full-stack design automation. We synthesize recent advances through a bottleneck-driven taxonomy that delineates the operating regimes and scaling trends where photonics can deliver end-to-end sustained benefits. A central theme is cross-layer co-design and workload-adaptive programmability to sustain high efficiency and versatility across evolving application domains at scale. We further argue that Electronic-Photonic Design Automation (EPDA) will be pivotal, enabling closed-loop co-optimization across simulation, inverse design, system modeling, and physical implementation. By charting a roadmap from laboratory prototypes to scalable, reproducible electronic-photonic ecosystems, this review aims to guide the CAS community toward an automated, system-centric era of photonic machine intelligence. |
| title | Harnessing Photonics for Machine Intelligence |
| topic | Optics Artificial Intelligence Hardware Architecture Emerging Technologies Machine Learning |
| url | https://arxiv.org/abs/2604.10841 |