Xiang Shuiying, Ren Zhenxing, Song Ziwei, Zhang Yahui, Guo Xingxing, Han Genquan, Hao Yue
IEEE Trans Neural Netw Learn Syst. 2021 Jun;32(6):2494-2505. doi: 10.1109/TNNLS.2020.3006263. Epub 2021 Jun 2.
We propose computing primitive for an all-optical spiking neural network (SNN) based on vertical-cavity surface-emitting lasers (VCSELs) for supervised learning by using biologically plausible mechanisms. The spike-timing-dependent plasticity (STDP) model was established based on the dynamics of the vertical-cavity semiconductor optical amplifier (VCSOA) subject to dual-optical pulse injection. The neuron-synapse self-consistent unified model of the all-optical SNN was developed, which enables reproducing the essential neuron-like dynamics and STDP function. Optical character numbers are trained and tested by the proposed fully VCSEL-based all-optical SNN. Simulation results show that the proposed all-optical SNN is capable of recognizing ten numbers by a supervised learning algorithm, in which the input and output patterns as well as the teacher signals of the all-optical SNN are represented by spatiotemporal fashions. Moreover, the lateral inhibition is not required in our proposed architecture, which is friendly to the hardware implementation. The system-level unified model enables architecture-algorithm codesigns and optimization of all-optical SNN. To the best of our knowledge, the computing primitive of an all-optical SNN based on VCSELs for supervised learning has not yet been reported, which paves the way toward fully VCSEL-based large-scale photonic neuromorphic systems with low power consumption.
我们提出基于垂直腔面发射激光器（VCSEL）为全光脉冲神经网络（SNN）计算基元，以利用生物合理机制进行监督学习。基于垂直腔半导体光放大器（VCSOA）在双光脉冲注入下的动力学建立了脉冲时间依赖可塑性（STDP）模型。开发了全光SNN的神经元 - 突触自洽统一模型，该模型能够再现基本的类神经元动力学和STDP功能。通过所提出的基于全VCSEL的全光SNN对光学字符数字进行训练和测试。仿真结果表明，所提出的全光SNN能够通过监督学习算法识别十个数字，其中全光SNN的输入和输出模式以及教师信号由时空方式表示。此外，在我们提出的架构中不需要横向抑制，这有利于硬件实现。系统级统一模型实现了全光SNN的架构 - 算法协同设计和优化。据我们所知，尚未报道基于VCSEL的用于监督学习的全光SNN的计算基元，这为实现低功耗的基于全VCSEL的大规模光子神经形态系统铺平了道路。
