  With the recent successes of neural networks (NN) to perform machine-learning
tasks, photonic-based NN designs may enable high throughput and low power
neuromorphic compute paradigms since they bypass the parasitic charging of
capacitive wires. Thus, engineering data-information processors capable of
executing NN algorithms with high efficiency is of major importance for
applications ranging from pattern recognition to classification. Our hypothesis
is therefore, that if the time-limiting electro-optic conversion of current
photonic NN designs could be postponed until the very end of the network, then
the execution time of the photonic algorithm is simple the delay of the
time-of-flight of photons through the NN, which is on the order of picoseconds
for integrated photonics. Exploring such all-optical NN, in this work we
discuss two independent approaches of implementing the optical perceptrons
nonlinear activation function based on nanophotonic structures exhibiting i)
induced transparency and ii) reverse saturated absorption. Our results show
that the all-optical nonlinearity provides about 3 and 7 dB extinction ratio
for the two systems considered, respectively, and classification accuracies of
an exemplary MNIST task of 97% and near 100% are found, which rivals that of
software based trained NNs, yet with ignored noise in the network. Together
with a developed concept for an all-optical perceptron, these findings point to
the possibility of realizing pure photonic NNs with potentially unmatched
throughput and even energy consumption for next generation information
processing hardware.
