A plethora of research advances have emerged in the fields of optics and photonics that benefit from harnessing the power of machine learning. Specifically, there has been a revival of interest in optical computing hardware, due to its potential advantages for machine learning tasks in terms of parallelization, power efficiency and computation speed. Diffractive Deep Neural Networks (D2NNs) form such an optical computing framework, which benefits from deep learning-based design of successive diffractive layers to all-optically process information as the input light diffracts through these passive layers. D2NNs have demonstrated success in various tasks, including e.g., object classification, spectral-encoding of information, optical pulse shaping and imaging, among others. Here, we significantly improve the inference performance of diffractive optical networks using feature engineering and ensemble learning. After independently training a total of 1252 D2NNs that were diversely engineered with a variety of passive input filters, we applied a pruning algorithm to select an optimized ensemble of D2NNs that collectively improve their image classification accuracy. Through this pruning, we numerically demonstrated that ensembles of N=14 and N=30 D2NNs achieve blind testing accuracies of 61.14% and 62.13%, respectively, on the classification of CIFAR-10 test images, providing an inference improvement of >16% compared to the average performance of the individual D2NNs within each ensemble. These results constitute the highest inference accuracies achieved to date by any diffractive optical neural network design on the same dataset and might provide a significant leapfrog to extend the application space of diffractive optical image classification and machine vision systems.

中文翻译：

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衍射光学网络的集成学习

受益于利用机器学习的力量，光学和光子学领域出现了大量研究进展。具体而言，由于其在并行化、功率效率和计算速度方面的机器学习任务的潜在优势，人们对光学计算硬件的兴趣重新燃起。衍射深度神经网络 (D2NN) 形成了这样一个光学计算框架，它受益于基于深度学习的连续衍射层设计，以便在输入光衍射通过这些无源层时对信息进行全光学处理。D2NN 已在各种任务中取得成功，包括对象分类、信息的光谱编码、光脉冲整形和成像等。这里，我们使用特征工程和集成学习显着提高了衍射光学网络的推理性能。在独立训练了总共 1252 个 D2NN 后，这些 D2NN 使用各种无源输入滤波器进行了多样化设计，我们应用修剪算法来选择优化的 D2NN 集合，共同提高其图像分类精度。通过这种剪枝，我们在数值上证明了 N=14 和 N=30 D2NN 的集成在 CIFAR-10 测试图像的分类上分别实现了 61.14% 和 62.13% 的盲测试准确率，相比之下提供了 >16% 的推理改进每个集成中单个 D2NN 的平均性能。