计算机科学
维数(图论)
生成语法
人工智能
生成模型
计算机体系结构
炸薯条
卓越
语义记忆
人机交互
计算机工程
功率(物理)
加速度
光子学
深度学习
人工神经网络
机器学习
光学计算
语义学(计算机科学)
机器视觉
能量(信号处理)
计算机视觉
高效能源利用
节点(物理)
伪装
特征提取
模式识别(心理学)
作者
Yitong Chen,Xinyue Sun,Longtao Tan,Yizhou Jiang,Yin Zhou,Wenjun Zhang,Guangtao Zhai
出处
期刊:Science
[American Association for the Advancement of Science]
日期:2025-12-18
卷期号:390 (6779): 1259-1265
被引量:3
标识
DOI:10.1126/science.adv7434
摘要
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.
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