  Deep neural networks with applications from computer vision and image
processing to medical diagnosis are commonly implemented using clock-based
processors, where computation speed is limited by the clock frequency and the
memory access time. Advances in photonic integrated circuits have enabled
research in photonic computation, where, despite excellent features such as
fast linear computation, no integrated photonic deep network has been
demonstrated to date due to the lack of scalable nonlinear functionality and
the loss of photonic devices, making scalability to a large number of layers
challenging. Here we report the first integrated end-to-end photonic deep
neural network (PDNN) that performs instantaneous image classification through
direct processing of optical waves. Images are formed on the input pixels and
optical waves are coupled into nanophotonic waveguides and processed as the
light propagates through layers of neurons on-chip. Each neuron generates an
optical output from input optical signals, where linear computation is
performed optically and the nonlinear activation function is realised
opto-electronically. The output of a laser coupled into the chip is uniformly
distributed among all neurons within the network providing the same per-neuron
supply light. Thus, all neurons have the same optical output range enabling
scalability to deep networks with large number of layers. The PDNN chip is used
for 2- and 4-class classification of handwritten letters achieving accuracies
of higher than 93.7% and 90.3%, respectively, with a computation time less than
one clock cycle of state-of-the-art digital computation platforms. Direct
clock-less processing of optical data eliminates photo-detection, A/D
conversion, and the requirement for a large memory module, enabling
significantly faster and more energy-efficient neural networks for the next
generations of deep learning systems.
