All-optical synthesis chip for large-scale intelligent semantic vision generation
Yitong Chen, Xinyue Sun, Longtao Tan, Yizhou Jiang, Yin Zhou, Wenjun Zhang, Guangtao Zhai
Abstract
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.