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Description:

analogvnn 1.0.8

AnalogVNN

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

Install PyTorch
Install AnalogVNN using pip

# Current stable release for CPU and GPU
pip install analogvnn

# For additional optional features
pip install analogvnn[full]

Usage:

Sample code with AnalogVNN: sample_code.py
Sample code without
AnalogVNN: sample_code_non_analog.py
Sample code with AnalogVNN and
Logs: sample_code_with_logs.py
Jupyter
Notebook: AnalogVNN_Demo.ipynb

Abstract

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.
AnalogVNN Paper: https://doi.org/10.1063/5.0134156
Citing AnalogVNN
We would appreciate if you cite the following paper in your publications for which you used AnalogVNN:
@article{shah2023analogvnn,
title={AnalogVNN: A fully modular framework for modeling and optimizing photonic neural networks},
author={Shah, Vivswan and Youngblood, Nathan},
journal={APL Machine Learning},
volume={1},
number={2},
year={2023},
publisher={AIP Publishing}
}

Or in textual form:
Vivswan Shah, and Nathan Youngblood. "AnalogVNN: A fully modular framework for modeling
and optimizing photonic neural networks." APL Machine Learning 1.2 (2023).
DOI: 10.1063/5.0134156