论文标题
类似物：用于建模和优化光子神经网络的完全模块化框架
AnalogVNN: A fully modular framework for modeling and optimizing photonic neural networks
论文作者
论文摘要
Analogvnn是建立在Pytorch上的模拟框架，可以模拟光电噪声的影响，有限的精度和光子神经网络加速器中存在的信号归一化。我们使用该框架来训练和优化具有多达9层和约170万参数的线性和卷积神经网络，同时洞悉模拟光子神经网络中的归一化，激活功能，降低精度和噪声影响精度。通过遵循Pytorch中存在的相同层结构设计，模拟框架允许用户将大多数数字神经网络模型转换为其模拟对应物，只需几行代码，就可以充分利用开源优化，深度学习和GPU加速器库。代码可从https://analogvnn.github.io获得。
AnalogVNN, a simulation framework built on PyTorch which can simulate the effects of optoelectronic noise, limited precision, and signal normalization present in photonic neural network accelerators. We use this framework to train and optimize linear and convolutional neural networks with up to 9 layers and ~1.7 million parameters, while gaining insights into how normalization, activation function, reduced precision, and noise influence accuracy in analog photonic neural networks. By following the same layer structure design present in PyTorch, the AnalogVNN framework allows users to convert most digital neural network models to their analog counterparts with just a few lines of code, taking full advantage of the open-source optimization, deep learning, and GPU acceleration libraries available through PyTorch. Code is available at https://analogvnn.github.io
