论文标题
具有组件不确定的光学神经网络的设计
Design of optical neural networks with component imprecisions
论文作者
论文摘要
为了设计可扩展的，抗故障的光学神经网络（ONN），我们研究了建筑设计对ONNS鲁棒性不精确组件的效果的影响。我们训练两个ONN（一个具有更可调的设计（Gridnet），一个具有更好容错的耐受性（FFTNET））来对手写数字进行分类。当没有任何缺陷的情况下模拟时，网格网的准确性比FFTNet的精度更好（〜98％）（〜95％）。但是，在其光子组件的少量误差下，耐受的FFTNET越多地超过了网格网。我们进一步提供了ONNS对不同级别和类型不确定类型的敏感性的彻底定量和定性分析。我们的结果提供了耐故障的原则设计指南，以及进一步研究的基础。
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.
