Hand-written letters classification measurement data

Deep neural networks with applications from computer vision to medical
diagnosis1-5 are commonly implemented using clock-based processors6-14,
where computation speed is mainly limited by the clock frequency and the
memory access time. In the optical domain, despite advances in photonic
computation15-17, the lack of scalable on-chip optical nonlinearity and
the loss of photonic devices limit the scalability of optical deep
networks. Here we report the first integrated end-to-end photonic deep
neural network (PDNN) that performs sub-nanosecond image classification
through direct processing of the optical waves impinging on the on-chip
pixel array as they propagate through layers of neurons. Within each
neuron, linear computation is performed optically and the nonlinear
activation function is realised opto-electronically, enabling a
classification time of under 570 ps, which is comparable with a single
clock-cycle of state-of-the-art digital platforms. A uniformly distributed
supply light provides the same per-neuron optical output range enabling
scalability to large-scale PDNNs. Two- and four-class classification of
handwritten letters with accuracies of higher than 93.8% and 89.8% are
demonstrated, respectively. Direct clock-less processing of optical data
eliminates analogue-to-digital conversion and the requirement for a large
memory module, enabling faster and more energy-efficient neural networks
for the next generations of deep learning systems.