We propose and experimentally demonstrate a large-scale, high-performance photonic computing platform that simultaneously combines light scattering and optical nonlinearity. The core processing unit consists in a disordered polycrystalline lithium niobate slab bottom-up assembled from nanocrystals. Assisted by random quasiphase-matching, nonlinear speckles are generated as the complex interplay between the simultaneous linear random scattering and the second-harmonic generation based on the quadratic optical nonlinearity of the material. Compared to linear random projection, such nonlinear feature extraction demonstrates universal performance improvement across various machine learning tasks in image classification, univariate and multivariate regression, and graph classification.
Numerous applications in science and technology nowadays utilize deep learning to tackle challenging computational tasks. With the increasing demand for deep learning, high-speed and energy-efficient accelerators are urgently needed. Although electronic accelerators are flexible, optical computers holds great promise due to their potential for massive parallelism and low power consumption. However, optical computing platforms demonstrated so far have mostly been limited to relatively small-scale computing tasks, despite the potential for scalability. Here, we propose and demonstrate a hardware-efficient design that allows deployment of a reconfigurable deep neural network (DNN) architecture without a direct isomorphism to standard DNN designs. Our proposed system is scalable and supports larger-scale computing. Our system realizes an optical neural network (ONN) using a digital micromirror device (DMD) for encoding data and trainable parameters, a complex medium for random complex weight mixing, and a camera for nonlinear activation and optical readout. A straight-through estimator enables backpropagation, even with a DMD as a binary encoding device. With this ONN as an elementary building block and automating the search for neural architectures, we can build complex and deep ONNs for a range of large-scale computing tasks, such as 3D medical image classification. The architecture-optimized deep ONNs are deployed by time-multiplexing data streams in one system. Our system enables large-scale training and inference in situ. Furthermore, we demonstrate that our system is capable of achieving task accuracies close to that of state-of-the-art benchmarks with more complex architectures implemented in silico.
We developed and implemented a deep optical neural network (ONN) design capable of performing large-scale training and inference in situ. For each elementary building block in the ONN, we introduce trainable parameters in a programmable device, weight mixing with a diffuser, and nonlinear detection on the camera for activation and optical readout. With automated reconfigurable neural architecture search, we optimized the architecture of deep ONNs that can perform multiple tasks at high speed and at large scale. The task accuracies achieved by our experiments are close to state-of-the-art benchmarks with conventional multilayer neural networks.
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