Li Yuhang, Li Jingxi, Ozcan Aydogan
Electrical & Computer Engineering Department, University of California, Los Angeles, CA, 90095, USA.
Bioengineering Department, University of California, Los Angeles, CA, 90095, USA.
Light Sci Appl. 2024 Jul 23;13(1):173. doi: 10.1038/s41377-024-01529-8.
Nonlinear encoding of optical information can be achieved using various forms of data representation. Here, we analyze the performances of different nonlinear information encoding strategies that can be employed in diffractive optical processors based on linear materials and shed light on their utility and performance gaps compared to the state-of-the-art digital deep neural networks. For a comprehensive evaluation, we used different datasets to compare the statistical inference performance of simpler-to-implement nonlinear encoding strategies that involve, e.g., phase encoding, against data repetition-based nonlinear encoding strategies. We show that data repetition within a diffractive volume (e.g., through an optical cavity or cascaded introduction of the input data) causes the loss of the universal linear transformation capability of a diffractive optical processor. Therefore, data repetition-based diffractive blocks cannot provide optical analogs to fully connected or convolutional layers commonly employed in digital neural networks. However, they can still be effectively trained for specific inference tasks and achieve enhanced accuracy, benefiting from the nonlinear encoding of the input information. Our results also reveal that phase encoding of input information without data repetition provides a simpler nonlinear encoding strategy with comparable statistical inference accuracy to data repetition-based diffractive processors. Our analyses and conclusions would be of broad interest to explore the push-pull relationship between linear material-based diffractive optical systems and nonlinear encoding strategies in visual information processors.
利用各种形式的数据表示可以实现光学信息的非线性编码。在此，我们分析了基于线性材料的衍射光学处理器中可采用的不同非线性信息编码策略的性能，并阐明了它们与最先进的数字深度神经网络相比的实用性和性能差距。为了进行全面评估，我们使用不同的数据集来比较更易于实现的非线性编码策略（例如相位编码）与基于数据重复的非线性编码策略的统计推理性能。我们表明，衍射体积内的数据重复（例如通过光学腔或输入数据的级联引入）会导致衍射光学处理器丧失通用线性变换能力。因此，基于数据重复的衍射模块无法为数字神经网络中常用的全连接层或卷积层提供光学模拟。然而，受益于输入信息的非线性编码，它们仍可针对特定推理任务进行有效训练并提高准确性。我们的结果还表明，无数据重复的输入信息相位编码提供了一种更简单的非线性编码策略，其统计推理准确性与基于数据重复的衍射处理器相当。我们的分析和结论对于探索视觉信息处理器中基于线性材料的衍射光学系统与非线性编码策略之间的推拉关系具有广泛的意义。
