https://github.com/fancompute/neuroptica
Flexible simulation package for optical neural networks
https://github.com/fancompute/neuroptica
machine-learning nanophotonics neural-network optics photonics
Last synced: 5 months ago
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Flexible simulation package for optical neural networks
- Host: GitHub
- URL: https://github.com/fancompute/neuroptica
- Owner: fancompute
- License: mit
- Created: 2018-10-12T19:00:46.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2020-04-22T21:07:20.000Z (about 6 years ago)
- Last Synced: 2025-09-28T18:26:56.928Z (9 months ago)
- Topics: machine-learning, nanophotonics, neural-network, optics, photonics
- Language: Python
- Homepage: https://doi.org/10.1109/JSTQE.2019.2930455
- Size: 7.22 MB
- Stars: 249
- Watchers: 22
- Forks: 43
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# neuroptica [](https://neuroptica.readthedocs.io/en/latest/?badge=latest) [](https://travis-ci.com/fancompute/neuroptica)
`neuroptica` is a flexible chip-level simulation platform for [nanophotonic neural networks](https://arxiv.org/abs/1903.04579) written in Python/NumPy. It provides a wide range of abstracton levels for simulating optical NN's: the lowest-level functionality allows you to manipulate the arrangement and properties of individual phase shifters on a simulated chip, and the highest-level features provide a Keras-like API for designing optical NN by stacking network layers.
## Installation
The easiest way to get started with `neuroptica` is to install directly from the Python package manager:
```
pip install neuroptica
```
Alternately, you can clone the repository source code and edit it as needed with:
```
git clone https://github.com/fancompute/neuroptica.git
pip install -e neuroptica
```
To run unit tests, use `- python -m unittest discover -v` from the root package directory.
## Getting started
For an overview of `neuroptica`, read the [documentation](https://neuroptica.readthedocs.io). Example notebooks are included in the [`neuroptica-notebooks`](https://github.com/fancompute/neuroptica-notebooks) repository:
- [Planar data classification using electro-optic activation functions](https://github.com/fancompute/neuroptica-notebooks/blob/master/neuroptica_demo.ipynb)
## Citing
`neuroptica` was written by [Ben Bartlett](https://github.com/bencbartlett), [Momchil Minkov](https://github.com/momchilmm), [Tyler Hughes](https://github.com/twhughes), and [Ian Williamson](https://github.com/ianwilliamson). If you find this useful for your research, please cite the GitHub repository and/or the JSQTE paper:
```
@misc{Bartlett2019Neuroptica,
author = {Ben Bartlett and Momchil Minkov and Tyler Hughes and Ian A. D. Williamson},
title = {Neuroptica: Flexible simulation package for optical neural networks},
year = {2019},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/fancompute/neuroptica}},
commit = {06484f698ee038eeb128cdfbd4c59a7e96185bb4}
}
```
```
@article{Williamson2019Reprogrammable,
author={I. A. D. Williamson and T. W. Hughes and M. Minkov and B. Bartlett and S. Pai and S. Fan},
journal={IEEE Journal of Selected Topics in Quantum Electronics},
title={Reprogrammable Electro-Optic Nonlinear Activation Functions for Optical Neural Networks},
year={2020},
volume={26},
number={1},
pages={1-12}
}
```