基于超光学的衍射学习机用于角度复用多任务处理

中文摘要 AI 辅助

全光学衍射神经网络（DNNs）具有低延迟、低能耗和大规模并行性的优点。然而，它们在网络规模和多任务处理方面的可扩展性仍然有限。在此，我们实验展示了一种大规模基于超表面的衍射学习机，用于角度复用多任务处理。我们的系统采用基于超表面的反射腔来实现一个五层DNN，该DNN在216 mm^3的紧凑体积内包含1.15亿个固定的、未经训练的衍射节点。通过将这种大规模、未经训练的DNN与仅含7,680个参数的轻量级可训练数字后端集成，我们的系统在标准全尺寸CIFAR-10数据集上实验达到了95.3%的准确率，显著优于以往的光学神经网络，并与大规模数字模型相媲美。利用一种新颖的入射角复用方案，我们的系统实验实现了11个任务的并行处理，性能下降可忽略不计，代表了迄今为止报道的最大多任务处理能力。我们的工作为高性能边缘计算提供了一种可扩展且节能的解决方案。

英文摘要

All-optical diffractive neural networks (DNNs) offer low latency, low energy consumption, and massive parallelism. However, their scalability in terms of network scale and multi-task processing remains limited. Here, we experimentally demonstrate a large-scale metasurface-based diffractive learning machine for angle-multiplexed multi-task processing. Our system employs a metasurface-based reflection cavity to realize a five-layer DNN comprising 115 million fixed, untrained diffractive nodes within a compact volume of 216 mm^3. By integrating this large-scale, untrained DNN with a lightweight, trainable digital backend containing only 7,680 parameters, our system experimentally achieves an accuracy of 95.3% on the standard full-scale CIFAR-10 dataset, significantly outperforming previous optical neural networks and rivaling large-scale digital models. Using a novel incident-angle-multiplexing scheme, our system experimentally achieves parallel processing of 11 tasks with negligible performance degradation, representing the largest multi-task processing capacity reported to date. Our work provides a scalable and energy-efficient solution for high-performance edge computing.

发表机构

• The Chinese University of Hong Kong（香港中文大学）
• Tsinghua University（清华大学）
• Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳））

机构由 AI 辅助整理，请以论文原文为准。