A review: Photonics devices, architectures, and algorithms for optical neural computing
Shuiying Xiang, Yanan Han, Ziwei Song, Xingxing Guo, Yahui Zhang, Zhenxing Ren, Suhong Wang, Yuanting Ma, Weiwen Zou, Bowen Ma, Shaofu Xu, Jianji Dong, Hailong Zhou, Quansheng Ren, Tao Deng, Yan Liu, Genquan Han, Yue Hao
Abstract
Abstract The explosive growth of data and information has motivated various emerging non-von Neumann computational approaches in the More-than-Moore era. Photonics neuromorphic computing has attracted lots of attention due to the fascinating advantages such as high speed, wide bandwidth, and massive parallelism. Here, we offer a review on the optical neural computing in our research groups at the device and system levels. The photonics neuron and photonics synapse plasticity are presented. In addition, we introduce several optical neural computing architectures and algorithms including photonic spiking neural network, photonic convolutional neural network, photonic matrix computation, photonic reservoir computing, and photonic reinforcement learning. Finally, we summarize the major challenges faced by photonic neuromorphic computing, and propose promising solutions and perspectives.