AnalogVNN: A Fully Modular Framework for Photonic Analog Neural Networks

Abstract

Optimal hyperparameters and inference accuracy for analog-based deep learning hardware is highly dependent on system architecture and component noise. We present AnalogVNN as a fully modular framework to easily model and train arbitrary analog photonic neural networks using the simple modular layer structures of PyTorch.