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TorchFI is a fault injection framework build on top of Pytorch for research purposes.

• Installation
  • Clone Project
  • Install Dependencies
• Citation
• License
• Acknowledgments

Installation

Clone Project

  git clone git@github.com:bfgoldstein/torchfi.git

Install Dependencies

We highly recommend installing an Anaconda environment.

conda env create -f torchfi.yml
conda activate torchfi
cd ${PROJECT_PATH}
export PYTHON_PATH=$PYTHON_PATH:${PROJECT_PATH}

Examples/Experiments

Check out this torchFI examples using AlexNet, ResNet18 and ResNet50 model.

Pruned Models

All pruned models to run the above experiments can be download at DeGirum and Distiller Model Zoo.

Citation

Please cite XXX in your publications if it helps your research:

@INPROCEEDINGS{goldstein2019,
  Author = {Goldstein, Brunno and Srinivasan, Sudarshan and Mellempudi, Naveen K and Das, Dipankar and Santiago, Leandro and Ferreira, Victor C. and Solon, N. and Kundu, Sandip and França, Felipe M. G.},
  Booktitle={2020 IEEE 11th Latin American Symposium on Circuits Systems (LASCAS)},
  Title = {Reliability Evaluation of Compressed Deep Learning Models},
  Year = {2020},
  Keywords={resilience, soft error, transient fault, neural network, deep learning}
}

License

TorchFI code is released under the Apache license 2.0.

Acknowledgments

• PyTorch - Python package for fast tensors computation and DNNs execution
• Distiller - Post-quantization code