Photonic unsupervised learning variational autoencoder for high-throughput and low-latency image transmission
Yitong Chen, Tiankuang Zhou, Jiamin Wu, Hui Qiao, Xing Lin, Lu Fang, Qionghai Dai
Abstract
Following the explosive growth of global data, there is an ever-increasing demand for high-throughput processing in image transmission systems. However, existing methods mainly rely on electronic circuits, which severely limits the transmission throughput. Here, we propose an end-to-end all-optical variational autoencoder, named photonic encoder-decoder (PED), which maps the physical system of image transmission into an optical generative neural network. By modeling the transmission noises as the variation in optical latent space, the PED establishes a large-scale high-throughput unsupervised optical computing framework that integrates main computations in image transmission, including compression, encryption, and error correction to the optical domain. It reduces the system latency of computation by more than four orders of magnitude compared with the state-of-the-art devices and transmission error ratio by 57% than on-off keying. Our work points to the direction for a wide range of artificial intelligence-based physical system designs and next-generation communications.