Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Design of optical neural networks with component imprecisions   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.

22
The categories of this paper are: chao-dyn
18
The categories of this paper are: econ
2
The categories of this paper are: math
20
The categories of this paper are: acc-phys
13
The categories of this paper are: quant-ph
9
The categories of this paper are: nlin
26
The categories of this paper are: bayes-an
31
The categories of this paper are: ao-sci
6
The categories of this paper are: astro-ph
35
The categories of this paper are: mtrl-th
7
The categories of this paper are: hep-th
12
The categories of this paper are: nucl-th
16
The categories of this paper are: stat
17
The categories of this paper are: q-fin
0
The categories of this paper are: hep-ph
8
The categories of this paper are: hep-ex