论文标题
光学神经常规微分方程
Optical Neural Ordinary Differential Equations
论文作者
论文摘要
增加片上光子神经网络（PNN）的层数对于改善其模型性能至关重要。但是，网络隐藏层的连续级联导致更大的集成光子芯片区域。为了解决这个问题，我们提出了光学神经常规微分方程（ON-ON-ON-OD-ON-OD）架构，该体系结构参数化了使用光ode求解器的隐藏层的连续动力学。 On-Ode包括PNN，然后是光子积分器和光学反馈回路，可以将其配置为表示残留的神经网络（RESNET）和复发性神经网络，并有效地降低了芯片面积占用率。对于基于干扰的光电非线性隐藏层，数值实验表明，单个隐藏层On-On-On-On-On-On-One可以达到与图像分类任务中的两层光学重新连接大致相同的精度。此外，ONODE提高了基于衍射的全光线性隐藏层的模型分类精度。以高度精确度进一步应用了On-Eod的时间相关动力学属性。
Increasing the layer number of on-chip photonic neural networks (PNNs) is essential to improve its model performance. However, the successively cascading of network hidden layers results in larger integrated photonic chip areas. To address this issue, we propose the optical neural ordinary differential equations (ON-ODE) architecture that parameterizes the continuous dynamics of hidden layers with optical ODE solvers. The ON-ODE comprises the PNNs followed by the photonic integrator and optical feedback loop, which can be configured to represent residual neural networks (ResNet) and recurrent neural networks with effectively reduced chip area occupancy. For the interference-based optoelectronic nonlinear hidden layer, the numerical experiments demonstrate that the single hidden layer ON-ODE can achieve approximately the same accuracy as the two-layer optical ResNet in image classification tasks. Besides, the ONODE improves the model classification accuracy for the diffraction-based all-optical linear hidden layer. The time-dependent dynamics property of ON-ODE is further applied for trajectory prediction with high accuracy.
