Diffractive Neural Networks (DNNs) leverage the power of light to enhance computational performance in machine learning, offering a pathway to high-speed, low-energy, and large-scale neural information processing. However, most existing DNN architectures are optimized for single tasks and thus lack the flexibility required for the simultaneous execution of multiple tasks within a unified artificial intelligence platform. In this work, we utilize the polarization and wavelength degrees of freedom of light to achieve optical multi-task identification using the MNIST, FMNIST, and KMNIST datasets. Employing bilayer cascaded metasurfaces, we construct dual-channel DNNs capable of simultaneously classifying two tasks, using polarization and wavelength multiplexing schemes through a meta-atom library. Numerical evaluations demonstrate performance accuracies comparable to those of individually trained single-channel, single-task DNNs. Extending this approach to three-task parallel recognition reveals an expected performance decline yet maintains satisfactory classification accuracies of greater than 80 % for all tasks. We further introduce a novel end-to-end joint optimization framework to redesign the three-task classifier, demonstrating substantial improvements over the meta-atom library design and offering the potential for future multi-channel DNN designs. Our study could pave the way for the development of ultrathin, high-speed, and high-throughput optical neural computing systems.

中文翻译：

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利用多路复用超表面进行全光学衍射处理器的多任务学习


衍射神经网络 （DNN） 利用光的力量来提高机器学习的计算性能，为高速、低能耗和大规模神经信息处理提供了途径。然而，大多数现有的 DNN 架构都是针对单个任务进行了优化的，因此缺乏在统一的人工智能平台中同时执行多个任务所需的灵活性。在这项工作中，我们利用光的偏振和波长自由度，使用 MNIST、FMNIST 和 KMNIST 数据集实现光学多任务识别。采用双层级联超表面，我们构建了双通道 DNN，能够同时对两个任务进行分类，通过超原子库使用偏振和波长多路复用方案。数值评估表明，性能准确性与单独训练的单通道、单任务 DNN 相当。将这种方法扩展到三任务并行识别可以揭示预期的性能下降，但对于所有任务，分类准确率保持在 80% 以上，令人满意。我们进一步引入了一种新的端到端联合优化框架来重新设计三任务分类器，展示了对元原子库设计的实质性改进，并为未来的多通道 DNN 设计提供了潜力。我们的研究可能为超薄、高速和高通量光学神经计算系统的发展铺平道路。