  Optical implementation of artificial neural networks has been attracting
great attention due to its potential in parallel computation at speed of light.
Although all-optical deep neural networks (AODNNs) with a few neurons have been
experimentally demonstrated with acceptable errors recently, the feasibility of
large scale AODNNs remains unknown because error might accumulate inevitably
with increasing number of neurons and connections. Here, we demonstrate a
scalable AODNN with programmable linear operations and tunable nonlinear
activation functions. We verify its scalability by measuring and analyzing
errors propagating from a single neuron to the entire network. The feasibility
of AODNNs is further confirmed by recognizing handwritten digits and fashions
respectively.
