# AnalogVNN
[](https://arxiv.org/abs/2210.10048)
[](https://doi.org/10.1063/5.0134156)
[](https://colab.research.google.com/github/Vivswan/AnalogVNN/blob/release/docs/_static/AnalogVNN_Demo.ipynb)
[](https://badge.fury.io/py/analogvnn)
[](https://analogvnn.readthedocs.io/en/stable/?badge=stable)
[](https://badge.fury.io/py/analogvnn)
[](https://opensource.org/licenses/MPL-2.0)
Documentation: [https://analogvnn.readthedocs.io/](https://analogvnn.readthedocs.io/)
## Installation:
- Install [PyTorch](https://pytorch.org/)
- Install AnalogVNN using [pip](https://pypi.org/project/analogvnn/)
```bash
# Current stable release for CPU and GPU
pip install analogvnn
# For additional optional features
pip install analogvnn[full]
```
## Usage:
[](https://colab.research.google.com/github/Vivswan/AnalogVNN/blob/release/docs/_static/AnalogVNN_Demo.ipynb)
- Sample code with AnalogVNN: [sample_code.py](https://github.com/Vivswan/AnalogVNN/blob/release/sample_code.py)
- Sample code without
AnalogVNN: [sample_code_non_analog.py](https://github.com/Vivswan/AnalogVNN/blob/release/sample_code_non_analog.py)
- Sample code with AnalogVNN and
Logs: [sample_code_with_logs.py](https://github.com/Vivswan/AnalogVNN/blob/release/sample_code_with_logs.py)
- Jupyter
Notebook: [AnalogVNN_Demo.ipynb](https://github.com/Vivswan/AnalogVNN/blob/release/docs/_static/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](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:
```bibtex
@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:
```text
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
```
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"description": "# AnalogVNN\n\n[](https://arxiv.org/abs/2210.10048)\n[](https://doi.org/10.1063/5.0134156)\n[](https://colab.research.google.com/github/Vivswan/AnalogVNN/blob/release/docs/_static/AnalogVNN_Demo.ipynb)\n\n[](https://badge.fury.io/py/analogvnn)\n[](https://analogvnn.readthedocs.io/en/stable/?badge=stable)\n[](https://badge.fury.io/py/analogvnn)\n[](https://opensource.org/licenses/MPL-2.0)\n\nDocumentation: [https://analogvnn.readthedocs.io/](https://analogvnn.readthedocs.io/)\n\n## Installation:\n\n- Install [PyTorch](https://pytorch.org/)\n- Install AnalogVNN using [pip](https://pypi.org/project/analogvnn/)\n\n```bash\n # Current stable release for CPU and GPU\n pip install analogvnn\n \n # For additional optional features\n pip install analogvnn[full]\n```\n\n## Usage:\n\n[](https://colab.research.google.com/github/Vivswan/AnalogVNN/blob/release/docs/_static/AnalogVNN_Demo.ipynb)\n\n- Sample code with AnalogVNN: [sample_code.py](https://github.com/Vivswan/AnalogVNN/blob/release/sample_code.py)\n- Sample code without\n AnalogVNN: [sample_code_non_analog.py](https://github.com/Vivswan/AnalogVNN/blob/release/sample_code_non_analog.py)\n- Sample code with AnalogVNN and\n Logs: [sample_code_with_logs.py](https://github.com/Vivswan/AnalogVNN/blob/release/sample_code_with_logs.py)\n- Jupyter\n Notebook: [AnalogVNN_Demo.ipynb](https://github.com/Vivswan/AnalogVNN/blob/release/docs/_static/AnalogVNN_Demo.ipynb)\n\n## Abstract\n\n[//]: # ()\n\n\n\n**AnalogVNN** is a simulation framework built on PyTorch which can simulate the effects of\noptoelectronic noise, limited precision, and signal normalization present in photonic\nneural network accelerators. We use this framework to train and optimize linear and\nconvolutional neural networks with up to 9 layers and ~1.7 million parameters, while\ngaining insights into how normalization, activation function, reduced precision, and\nnoise influence accuracy in analog photonic neural networks. By following the same layer\nstructure design present in PyTorch, the AnalogVNN framework allows users to convert most\ndigital neural network models to their analog counterparts with just a few lines of code,\ntaking full advantage of the open-source optimization, deep learning, and GPU acceleration\nlibraries available through PyTorch.\n\nAnalogVNN Paper: [https://doi.org/10.1063/5.0134156](https://doi.org/10.1063/5.0134156)\n\n## Citing AnalogVNN\n\nWe would appreciate if you cite the following paper in your publications for which you used AnalogVNN:\n\n```bibtex\n@article{shah2023analogvnn,\n title={AnalogVNN: A fully modular framework for modeling and optimizing photonic neural networks},\n author={Shah, Vivswan and Youngblood, Nathan},\n journal={APL Machine Learning},\n volume={1},\n number={2},\n year={2023},\n publisher={AIP Publishing}\n}\n```\n\nOr in textual form:\n\n```text\nVivswan Shah, and Nathan Youngblood. \"AnalogVNN: A fully modular framework for modeling \nand optimizing photonic neural networks.\" APL Machine Learning 1.2 (2023).\nDOI: 10.1063/5.0134156\n```\n",
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