analogvnn 1.0.0rc1

A fully modular framework for modeling and optimizing analog/photonic neural networks

AnalogVNN

AnalogVNN Paper: https://arxiv.org/abs/2210.10048

Documentation: https://analogvnn.readthedocs.io/

Cite: Vivswan Shah, and Nathan Youngblood. "AnalogVNN: A fully modular framework for modeling and optimizing photonic neural networks." arXiv preprint arXiv:2210.10048 (2022).

Installation:

• Install PyTorch
• Install AnanlogVNN using pip

pip install analogvnn

AnalogVNN is 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.