An on-chip photonic deep neural network for image classification

Deep neural networks with applications from computer vision to medical diagnosis(1-5) are commonly implemented using clock-based processors(6-14), in which computation speed is mainly limited by the clock frequency and the memory access time. In the optical domain, despite advances in photonic computation(15-17), the lack of scalable on-chip optical non-linearity and the loss of photonic devices limit the scalability of optical deep networks. Here we report an 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. In each neuron, linear computation is performed optically and the non-linear activation function is realized opto-electronically, allowing 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, allowing scalability to large-scale PDNNs. Two-class and four-class classification of handwritten letters with accuracies higher than 93.8% and 89.8%, respectively, is demonstrated. Direct, clock-less processing of optical data eliminates analogue-to-digital conversion and the requirement for a large memory module, allowing faster and more energy efficient neural networks for the next generations of deep learning systems.

Automated summary

This study presents an integrated photonic deep neural network that performs image classification with sub-nanosecond classification time. Optical waves are directly processed to achieve linear computation in each neuron and opto-electronically realized non-linear activation functions, resulting in comparable computation speed and scalability with digital platforms.