MemTorch is a Simulation Framework for Memristive Deep Learning Systems, which integrates directly with the well-known PyTorch Machine Learning (ML) library. MemTorch is formally described in MemTorch: An Open-source Simulation Framework for Memristive Deep Learning Systems, which is openly accessible here.

MemTorch: An Open-source Simulation Framework for Memristive Deep Learning Systems has been published as an Original Software Publication (OSP) in the Neurocomputing journal here. We kindly ask that the following BibTeX entry is used to cite MemTorch, if you use it in your work.

MemTorch: An Open-source Simulation Framework for Memristive Deep Learning Systems

Corey Lammie, Wei Xiang, Bernabé Linares-Barranco, and Mostafa Rahimi Azghadi

Abstract: Memristive devices have shown great promise to facilitate the acceleration and improve the power efficiency of Deep Learning (DL) systems. Crossbar architectures constructed using these Resistive Random-Access Memory (RRAM) devices can be used to efficiently implement various in-memory computing operations, such as Multiply Accumulate (MAC) and unrolled-convolutions, which are used extensively in Deep Neural Networks (DNNs) and Convolutional Neural Networks (CNNs). However, memristive devices face concerns of aging and non-idealities, which limit the accuracy, reliability, and robustness of Memristive Deep Learning Systems (MDLSs), that should be considered prior to circuit-level realization. This Original Software Publication (OSP) presents MemTorch, an open-source framework for customized large-scale memristive DL simulations, with a refined focus on the co-simulation of device non-idealities. MemTorch also facilitates co-modelling of key crossbar peripheral circuitry. MemTorch adopts a modernized soft-ware engineering methodology and integrates directly with the well-known PyTorch Machine Learning (ML) library.

Installation

MemTorch can be installed from source using python setup.py install:

git clone --recursive https://github.com/coreylammie/MemTorch
cd MemTorch
python setup.py install

or using pip install ., as follows:

git clone --recursive https://github.com/coreylammie/MemTorch
cd MemTorch
pip install .

If CUDA is True in setup.py, CUDA Toolkit 10.1 and Microsoft Visual C++ Build Tools are required. If CUDA is False in setup.py, Microsoft Visual C++ Build Tools are required.

Alternatively, MemTorch can be installed using the pip package-management system:

pip install memtorch-cpu # Supports normal operation
pip install memtorch # Supports CUDA and normal operation

API & Example Usage

A complete API is avaliable here. To learn how to use MemTorch, and to reproduce results of ‘MemTorch: An Open-source Simulation Framework for Memristive Deep Learning Systems’, we provide numerous tutorials in the form of Jupyter notebooks here.

The best place to get started is here.

Current Issues and Feature Requests

Current issues, feature requests and improvements are welcome, and are tracked using: https://github.com/coreylammie/MemTorch/projects/1.

These should be reported here.

Contributing

Please follow the "fork-and-pull" Git workflow:

1. Fork the repo on GitHub.
2. Clone the project to your own machine using git clone --recursive.
3. Enter Development Mode using python setup.py develop in the cloned repository's directory.
4. Configure git pre-commit, black, isort, and clang-format using pip install pre-commit black isort && pre-commit install and apt install clang clang-format (for linux) or choco install llvm uncrustify cppcheck (for windows).
5. Commit changes to your own branch.
6. Push your work back up to your fork.
7. Submit a Pull request so that your changes can be reviewed.

Be sure to merge the latest from 'upstream' before making a pull request. This can be accomplished using git rebase master.

Citation

To cite MemTorch: An Open-source Simulation Framework for Memristive Deep Learning Systems, use the following BibTex entry:

@Article{Lammie2022,
  author   = {Corey Lammie and Wei Xiang and Bernabé Linares-Barranco and Mostafa Rahimi Azghadi},
  title    = {{MemTorch: An Open-source Simulation Framework for Memristive Deep Learning Systems}},
  journal  = {Neurocomputing},
  year     = {2022},
  issn     = {0925-2312},
  doi      = {https://doi.org/10.1016/j.neucom.2022.02.043},
  keywords = {Memristors, RRAM, Non-Ideal Device Characteristics, Deep Learning, Simulation Framework},
  url      = {https://www.sciencedirect.com/science/article/pii/S0925231222002053},
}

To cite this repository, use the following BibTex entry:

@software{corey_lammie_2020_3760696,
  author={Corey Lammie and Wei Xiang and Bernab\'e Linares-Barranco and Mostafa Rahimi Azghadi},
  title={{coreylammie/MemTorch: Initial Release}},
  month=Apr.,
  year={2020},
  publisher={Zenodo},
  doi={10.5281/zenodo.3760695},
  url={https://doi.org/10.5281/zenodo.3760696}
}

License

All code is licensed under the GNU General Public License v3.0. Details pertaining to this are available at: https://www.gnu.org/licenses/gpl-3.0.en.html.