All-optical synthesis chip for large-scale intelligent semantic vision generation
Large-scale generative artificial intelligence (AI) is facing a severe computing power shortage. Although photonic computing achieves excellence in decision tasks, its application in generative tasks remains formidable because of limited integration scale, time-consuming dimension conversions, and ground-truth-dependent training algorithms. We produced an all-optical chip for large-scale intelligent vision generation, named LightGen. By integrating millions of photonic neurons on a chip, varying network dimension through proposed optical latent space, and Bayes-based training algorithms, LightGen experimentally implemented high-resolution semantic image generation, denoising, style transfer, three-dimensional generation, and manipulation. Its measured end-to-end computing speed and energy efficiency were each more than two orders of magnitude greater than those of state-of-the-art electronic chips, paving the way for acceleration of large visual generative models.
Authors
- Liwen Tan (ORCID: https://orcid.org/0000-0002-5451-9356)
- Yizhou Jiang (ORCID: https://orcid.org/0000-0003-0541-2998)
- Guangtao Zhai (ORCID: https://orcid.org/0000-0001-8165-9322)
- Wenjun Zhang (ORCID: https://orcid.org/0000-0001-7973-8769)
- Yitong Chen (ORCID: https://orcid.org/0000-0003-4207-4959)
- Yin Zhou
- Xinyue Sun (ORCID: https://orcid.org/0009-0008-0529-4323)
Institutions
- Shanghai Jiao Tong University (CN)
- Tsinghua University (CN)
Publication Details
- Journal
- Science
- Published
- 2025-12-18
- DOI
- https://doi.org/10.1126/science.adv7434
- Citations
- 2
- Primary Topic
- Neural Networks and Reservoir Computing
- Type
- article
- Field-Weighted Citation Impact
- 4.96