For the benefit of designing scalable, fault resistant optical neural networks (ONNs), we investigate the effects architectural designs have on the ONNs' robustness to imprecise components. We train two ONNs -- one with a more tunable design (GridNet) and one with better fault tolerance (FFTNet) -- to classify handwritten digits. When simulated without any imperfections, GridNet yields a better accuracy (~98%) than FFTNet (~95%). However, under a small amount of error in their photonic components, the more fault tolerant FFTNet overtakes GridNet. We further provide thorough quantitative and qualitative analyses of ONNs' sensitivity to varying levels and types of imprecisions. Our results offer guidelines for the principled design of fault-tolerant ONNs as well as a foundation for further research.

中文翻译：

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具有分量不精确性的光神经网络设计

为了设计可扩展的、抗故障的光神经网络 (ONN)，我们研究了架构设计对 ONN 对不精确组件的鲁棒性的影响。我们训练了两个 ONN——一个具有更可调的设计 (GridNet) 和一个具有更好的容错性 (FFTNet)——来对手写数字进行分类。在没有任何缺陷的情况下进行模拟时，GridNet 产生比 FFTNet (~95%) 更好的精度 (~98%)。然而，在其光子组件出现少量误差的情况下，容错能力更强的 FFTNet 超过了 GridNet。我们进一步对 ONN 对不同级别和类型的不精确性的敏感性进行了全面的定量和定性分析。我们的结果为容错 ONN 的原则设计提供了指导，并为进一步研究奠定了基础。