neuroptica

neuroptica is a flexible chip-level simulation platform for nanophotonic neural networks 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. Example notebooks are included in the neuroptica-notebooks repository:

• Planar data classification using electro-optic activation functions

Citing

neuroptica was written by Ben Bartlett, Momchil Minkov, Tyler Hughes, and Ian Williamson. 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}
}