  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.
