Convolutional Neural Networks (CNNs) are employed in a plethora of fields, including computer vision, natural language processing, and speech recognition. We present an integrated photonic accelerator for CNNs based on the temporal-spatial interleaving of signals. This architecture supports 1D kernels, and can be extended to 2D convolutional kernels, providing scalability for complex networks. A supervised on-chip learning algorithm is employed to guarantee a reliable setting of convolutional weights against fabrication tolerances, thermal cross-talks, and changes in operating conditions. Overall, by leveraging photonics technology, the proposed accelerator significantly reduces hardware complexity while enabling high-speed processing and parallelism.
In this work, we consider a hybrid-coherent time-delay photonic reservoir, with a coherent input layer and an incoherent output layer, for post-processing signals from a 200 km, 28 GBaud PAM-4 transmission link. The amplitude and the phase of the transmission link are obtained through a coherent receiver and introduced as two independent encoding signals at the input of our photonic reservoir. We use the photodetected intensity of the reservoir’s response to train a linear classifier and perform the data recovery task. This hybrid-coherent reservoir exhibits a bit error rate of 10−4 , three orders of magnitude lower compared to the performance of the same photonic reservoir that processes only the amplitude information at the input.
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