  We propose a scalable photonic architecture for implementation of feedforward
and recurrent neural networks to perform the classification of handwritten
digits from the MNIST database. Our experiment exploits off-the-shelf optical
and electronic components to currently achieve a network size of 16,384 nodes.
Both network types are designed within the the reservoir computing paradigm
with randomly weighted input and hidden layers. Using various feature
extraction techniques (e.g. histograms of oriented gradients, zoning, Gabor
filters) and a simple training procedure consisting of linear regression and
winner-takes-all decision strategy, we demonstrate numerically and
experimentally that a feedforward network allows for classification error rate
of 1%, which is at the state-of-the-art for experimental implementations and
remains competitive with more advanced algorithmic approaches. We also
investigate recurrent networks in numerical simulations by explicitly
activating the temporal dynamics, and predict a performance improvement over
the feedforward configuration.
