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As the ultra-high frequency nature of lightwave corresponds to great potential in wideband signal processing, nextgeneration electronic information systems of surveillance, radar and communications is promised with photonic signal processing systems. However, the sophisticated photonic systems suffer from various hardware defects, which severely limit the performance of signal processing. By introducing the emerging deep learning technology into the photonic system, the hardware defects can be recovered by the trained neural networks. Using different modified neural networks, we have demonstrated high-accuracy photonic analog-to-digital converters, Brillouin instantaneous frequency measurement, and high-fidelity photonic radar receivers. The demonstrated systems with simple configurations can outperform the conventional photonic processing system with complex configurations. Note that the adoption of neural networks may cause additional time delay to the signal flow. Photonic neural network accelerators (PNNs) become a promising solution to realize real-time signal processing. We propose and experimentally demonstrate several system architectures of photonic convolutional neural networks. The photonic dot product unit architecture implements the basic operation in convolution neural networks. And an optical patching scheme is demonstrated to enhances the power efficiency of the input ports in PNNs. Performance evaluations show that the proposed PNN architectures possess potential advantages of energy efficiency and computational power. We believe that, by combining the technical advantages of photonic signal processing and PNN acceleration, intelligent photonic signal processing systems with high-performance real-time wideband signal processing capabilities can be realized. Moreover, the large-scale photonic integration technology promises the fabrication of such hybrid systems in the future.
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