CITATION.cff

cff-version: 1.2.0
title: 'AnalogVNN: A fully modular framework for modeling and optimizing photonic neural networks'
message: 'If you use this software, please cite it as below.'
preferred-citation:
  type: article
  authors:
    - given-names: Vivswan
      family-names: Shah
      email: vivswanshah@pitt.edu
      affiliation: University of Pittsburgh
    - family-names: Youngblood
      given-names: Nathan
      affiliation: University of Pittsburgh
  doi: "10.1063/5.0134156"
  journal: "APL Machine Learning"
  title: 'AnalogVNN: A fully modular framework for modeling and optimizing photonic neural networks'
  year: 2023
authors:
  - given-names: Vivswan
    family-names: Shah
    email: vivswanshah@pitt.edu
    affiliation: University of Pittsburgh
  - family-names: Youngblood
    given-names: Nathan
    affiliation: University of Pittsburgh
identifiers:
  - type: doi
    value: 10.1063/5.0134156
    description: >-
      The concept DOI for the collection containing
      all versions of the Citation File Format.
repository-code: 'https://github.com/Vivswan/AnalogVNN'
url: 'https://analogvnn.readthedocs.io/'
abstract: >-
  AnalogVNN, 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.
keywords:
  - photonics
  - neural-networks
  - analog-computing
  - deep-learning
license: MPL-2.0