A review: Photonics devices, architectures, and algorithms for optical neural computing
A review: Photonics devices, architectures, and algorithms for optical neural computing
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DOI：
10.1088/1674-4926/42/2/023105
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发表时间：
2021-02
影响因子：
5.1
通讯作者：
S. Xiang;Yanan Han;Z. Song;Xingxing Guo;Yahui Zhang;Z. Ren;Suhong Wang;Yuanting Ma;Weiwen Zo
中科院分区：
文献类型：
--
作者：
S. Xiang;Yanan Han;Z. Song;Xingxing Guo;Yahui Zhang;Z. Ren;Suhong Wang;Yuanting Ma;Weiwen Zo
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.
