  Light's ability to perform massive linear operations parallelly has recently
inspired numerous demonstrations of optics-assisted artificial neural networks
(ANN). However, a clear advantage of optics over purely digital ANN in a
system-level has not yet been established. While linear operations can indeed
be optically performed very efficiently, the lack of nonlinearity and signal
regeneration require high-power, low-latency signal transduction between optics
and electronics. Additionally, a large power is needed for the lasers and
photodetectors, which are often neglected in the calculation of energy
consumption. Here, instead of mapping traditional digital operations to optics,
we co-optimized a hybrid optical-digital ANN, that operates on incoherent
light, and thus amenable to operations under ambient light. Keeping the latency
and power constant between purely digital ANN and hybrid optical-digital ANN,
we identified a low-power/ latency regime, where an optical encoder provides
higher classification accuracy than a purely digital ANN. However, in that
regime, the overall classification accuracy is lower than what is achievable
with higher power and latency. Our results indicate that optics can be
advantageous over digital ANN in applications, where the overall performance of
the ANN can be relaxed to prioritize lower power and latency.
