Design of optical neural networks with component imprecisions
This work addresses the problem of designing scalable and fault-resistant ONNs for researchers and engineers in photonic computing, though it is incremental as it builds on existing ONN designs.
The paper investigated how architectural designs affect the robustness of optical neural networks (ONNs) to component imprecisions, finding that a more fault-tolerant design (FFTNet) outperformed a more tunable one (GridNet) under errors, with accuracies shifting from ~98% to ~95% without imperfections.
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.