  Compared to electronic accelerators, integrated silicon-photonic neural
networks (SP-NNs) promise higher speed and energy efficiency for emerging
artificial-intelligence applications. However, a hitherto overlooked problem in
SP-NNs is that the underlying silicon photonic devices suffer from intrinsic
optical loss and crosstalk noise, the impact of which accumulates as the
network scales up. Leveraging precise device-level models, this paper presents
the first comprehensive and systematic optical loss and crosstalk modeling
framework for SP-NNs. For an SP-NN case study with two hidden layers and 1380
tunable parameters, we show a catastrophic 84% drop in inferencing accuracy due
to optical loss and crosstalk noise.
