用位置编码实现光计算中的非线性，低功耗且无需复杂元件。
Nonlinear Computation with Linear Optics via Source-Position Encoding
- 通过数据驱动的光路位置编码，实现全线性光学系统中的非线性计算。
- 在分类任务中显著优于传统线性方法，性能接近标准人工神经网络。
- 适合追求低功耗、高能效的专用光学神经网络设计者使用。
光学计算系统为神经网络工作负载提供了替代硬件模型，但实现高效非线性——神经网络的关键需求——仍是光学领域的一大挑战。本文提出一种新方法，在完全线性介质中实现非线性计算。该方法功耗极低，仅需根据数据动态调整光学系统的空间位置输入。利用这一位置编码机制，我们构建了一个基于拓扑优化的全自动硬件设计框架，结合现代优化与机器学习进展，用于定制化光学神经网络。我们在机器学习分类任务上评估了所设计的光学系统，结果表明其性能显著优于线性方法，在多项任务中达到与标准人工神经网络相当的水平。
原文摘要 · Abstract (English)
Optical computing systems provide an alternate hardware model which appears to be aligned with the demands of neural network workloads. However, the challenge of implementing energy efficient nonlinearities in optics -- a key requirement for realizing neural networks -- is a conspicuous missing link. In this work we introduce a novel method to achieve nonlinear computation in fully linear media. Our method can operate at low power and requires only the ability to drive the optical system at a data-dependent spatial position. Leveraging this positional encoding, we formulate a fully automated, topology-optimization-based hardware design framework for extremely specialized optical neural networks, drawing on modern advancements in optimization and machine learning. We evaluate our optical designs on machine learning classification tasks: demonstrating significant improvements over linear methods, and competitive performance when compared to standard artificial neural networks.
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