发表机构
Shanghai Jiao Tong University; Aalto University; Fudan University; Zhangjiang Laboratory; North Ocean Photonics Co. Ltd.（上海交通大学; 阿尔托大学; 复旦大学; 张江实验室; 北海光子有限公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出光子时间处理器，通过时空对偶性实现可扩展的光学神经网络，完成分类、图像视频生成等任务，性能优于现有系统，为下一代机器智能提供了新途径。
AI 中文摘要
光学神经网络（ONNs）有望实现高吞吐量和高能效的人工智能，但迄今为止几乎所有实现方案都在空间维度上编码信息，要么采用自由空间阵列，要么采用集成波导网格，这使得神经元数量与物理组件数量绑定，且路由拓扑在制造时就被固定。本文表明，将计算转移到时间维度可使计算维度与硬件维度解耦。利用时空对偶性，我们完全在时域中实现光学衍射与干涉，使用薄膜铌酸锂调制器作为时间透镜和时间掩模，色散提供连续时间神经元之间的耦合。我们仅用单个光输入/输出端口实验验证了高阶复值矩阵-矩阵乘法，使计算维度远超通道数。通过引入光学反馈，我们将该平台扩展为通用神经网络，其中网络层、神经元数量和突触连接完全可编程且可原位训练。我们的时间衍射神经网络在多个分类基准以及图像和视频生成任务中得到成功验证。值得注意的是，使用该平台我们演示了全模拟生成管线，其中潜在变量直接取自放大自发辐射，因此生成路径中不存在任何数字采样或电子调制。此外，高分辨率图像和视频以高帧率生成，优于受调制器刷新限制的最先进光学生成系统。这些结果建立了统一的光子时间计算框架，为下一代机器智能提供了可扩展且可部署的途径。
英文摘要
Optical neural networks (ONNs) promise high-throughput and energy-efficient artificial intelligence, yet essentially all implementations so far encode information across space either in free-space arrays or in integrated waveguide meshes, tying the number of neurons to the number of physical components and fixes the routing topology at fabrication. Here we show that moving the computation into time decouples computational dimension from hardware dimension. Exploiting space-time duality, we implement optical diffraction and interference entirely in time domain, using thin-film lithium niobate modulators as time lenses and temporal masks, with chromatic dispersion providing the coupling between successive temporal neurons. We experimentally verify high-order, complex-valued matrix-matrix multiplications using just a single optical input/output port, scaling the computational dimensions far beyond the channel count. By incorporating optical feedback, we extend this platform into versatile neural networks, where the network layers, neuron numbers, and synaptic connections are fully programmable and in-situ trainable. Our temporal diffractive neural networks are successfully validated on various classification benchmarks, alongside image and video generation tasks. Notably, using this platform we demonstrate an all-analogue generative pipeline in which the latent variable is drawn directly from amplified spontaneous emission, so that no digital sampling or electronic modulation appears anywhere in the generative path. Furthermore, high-resolution images and videos are generated at high frame rates, outperforming state-of-the-art modulator-refresh-limited optical generative systems. These results establish a unified photonic temporal computing framework, providing a scalable and deployable pathway toward next-generation machine intelligence.
