High computational density nanophotonic media for machine learning inference
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866915348309082112 |
|---|---|
| author | Zhao, Zhenyu Pan, Yichen Xiang, Jinlong Zhang, Yujia He, An Zhao, Yaotian Chen, Youlve He, Yu Fang, Xinyuan Su, Yikai Gu, Min Guo, Xuhan |
| author_facet | Zhao, Zhenyu Pan, Yichen Xiang, Jinlong Zhang, Yujia He, An Zhao, Yaotian Chen, Youlve He, Yu Fang, Xinyuan Su, Yikai Gu, Min Guo, Xuhan |
| contents | Efficient machine learning inference is essential for the rapid adoption of artificial intelligence across various domains.On-chip optical computing has emerged as a transformative solution for accelerating machine learning tasks, owing to its ultra-low power consumption. However, enhancing the computational density of on-chip optical systems remains a significant challenge, primarily due to the difficulties in miniaturizing and integrating key optical interference components.In this work, we harness the potential of fabrication-constrained scattering optical computing within nanophotonic media to address these limitations.Central to our approach is the use of fabrication-aware inverse design techniques, which enable the realization of manufacturable on-chip scattering structures under practical constraints.This results in an ultra-compact optical neural computing architecture with an area of just 64 um2,representing a remarkable three orders of magnitude reduction in footprint compared to traditional optical neural networks. Our prototype, tested on the Iris flower dataset, achieved an experimental accuracy of 86.7%, closely matching the simulation benchmark.This breakthrough showcases a promising pathway toward ultra-dense, energy-efficient optical processors for scalable machine learning inference, significantly reducing both the hardware footprint, latency, and power consumption of next-generation AI applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_14269 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | High computational density nanophotonic media for machine learning inference Zhao, Zhenyu Pan, Yichen Xiang, Jinlong Zhang, Yujia He, An Zhao, Yaotian Chen, Youlve He, Yu Fang, Xinyuan Su, Yikai Gu, Min Guo, Xuhan Optics Efficient machine learning inference is essential for the rapid adoption of artificial intelligence across various domains.On-chip optical computing has emerged as a transformative solution for accelerating machine learning tasks, owing to its ultra-low power consumption. However, enhancing the computational density of on-chip optical systems remains a significant challenge, primarily due to the difficulties in miniaturizing and integrating key optical interference components.In this work, we harness the potential of fabrication-constrained scattering optical computing within nanophotonic media to address these limitations.Central to our approach is the use of fabrication-aware inverse design techniques, which enable the realization of manufacturable on-chip scattering structures under practical constraints.This results in an ultra-compact optical neural computing architecture with an area of just 64 um2,representing a remarkable three orders of magnitude reduction in footprint compared to traditional optical neural networks. Our prototype, tested on the Iris flower dataset, achieved an experimental accuracy of 86.7%, closely matching the simulation benchmark.This breakthrough showcases a promising pathway toward ultra-dense, energy-efficient optical processors for scalable machine learning inference, significantly reducing both the hardware footprint, latency, and power consumption of next-generation AI applications. |
| title | High computational density nanophotonic media for machine learning inference |
| topic | Optics |
| url | https://arxiv.org/abs/2506.14269 |