All-optical neural networks (AONNs) have emerged as a promising paradigm for ultrafast and energy-efficient computation. These networks typically consist of multiple serially connected layers between input and output layers--a configuration we term spatially series AONNs, with deep neural networks (DNNs) being the most prominent examples. However, such series architectures suffer from progressive signal degradation during information propagation and critically require additional nonlinearity designs to model complex relationships effectively. Here we propose a spatially parallel architecture for all-optical neural networks (SP-AONNs). Unlike series architecture that sequentially processes information through consecutively connected optical layers, SP-AONNs divide the input signal into identical copies fed simultaneously into separate optical layers. Through coherent interference between these parallel linear sub-networks, SP-AONNs inherently enable nonlinear computation without relying on active nonlinear components or iterative updates. We implemented a modular 4F optical system for SP-AONNs and evaluated its performance across multiple image classification benchmarks. Experimental results demonstrate that increasing the number of parallel sub-networks consistently enhances accuracy, improves noise robustness, and expands model expressivity. Our findings highlight spatial parallelism as a practical and scalable strategy for advancing the capabilities of optical neural computing.

翻译：全光神经网络（AONNs）已成为超高速与高能效计算的一种前景广阔的模式。这类网络通常在输入层与输出层之间包含多个串行连接的层——我们将这种配置称为空间串行AONNs，其中深度神经网络（DNNs）是最典型的代表。然而，此类串行架构在信息传播过程中会遭受渐进式信号衰减，且关键性地需要额外的非线性设计以有效建模复杂关系。本文提出一种空间并行架构的全光神经网络（SP-AONNs）。与通过连续连接的光学层顺序处理信息的串行架构不同，SP-AONNs将输入信号分割为多个相同副本，同时馈入独立的光学层。通过这些并行线性子网络之间的相干干涉，SP-AONNs无需依赖主动非线性元件或迭代更新即可实现非线性计算。我们为SP-AONNs搭建了模块化的4F光学系统，并在多个图像分类基准任务上评估其性能。实验结果表明，增加并行子网络数量能持续提升准确率、增强噪声鲁棒性并扩展模型表达能力。我们的研究揭示了空间并行机制是推进光学神经计算能力的一种实用且可扩展的策略。