1. [Publications](/publications) 2. PyTorchFI: A Runtime Perturbation Tool for DNNs # PyTorchFI: A Runtime Perturbation Tool for DNNs ![](/sites/default/files/styles/wide/public/pubs/2020-06_PyTorchFI%3A-A-Runtime/default.jpg?itok=j_P90ufZ) PyTorchFI is a runtime perturbation tool for deep neural networks (DNNs), implemented for the popular PyTorch deep learning platform. PyTorchFI enables users to perform perturbations on weights or neurons of DNNs at runtime. It is designed with the programmer in mind, providing a simple and easy-to-use API, requiring as little as three lines of code for use. It also provides an extensible interface, enabling researchers to choose from various perturbation models (or design their own custom models), which allows for the study of hardware error (or general perturbation) propagation to the software layer of the DNN output. Additionally, PyTorchFI is extremely versatile: we demonstrate how it can be applied to five different use cases for dependability and reliability research, including resiliency analysis of classification networks, resiliency analysis of object detection networks, analysis of models robust to adversarial attacks, training resilient models, and for DNN interpertability. This paper discusses the technical underpinnings and design decisions of PyTorchFI which make it an easy-to-use, extensible, fast, and versatile research tool. PyTorchFI is open-sourced and available for download via pip or github at: . ## Authors Abdulrahman Mahmoud (University of Illinois at Urbana-Champaign) Neeraj Aggarwal (University of Illinois at Urbana-Champaign) Alex Nobbe (University of Illinois at Urbana-Champaign) Jose Rodrigo Sanchez Vicarte (University of Illinois at Urbana-Champaign) Sarita V. Adve (University of Illinois at Urbana-Champaign) Christopher W. Fletcher (University of Illinois at Urbana-Champaign) [Iuri Frosio](/person/iuri-frosio) [Siva Hari](/person/siva-hari) ## Publication Date Monday, June 29, 2020 ## Published in [Workshop on Dependable and Secure Machine Learning](https://ieeexplore.ieee.org/document/9151812) ## Research Area [Artificial Intelligence and Machine Learning ](/research-area/machine-learning-artificial-intelligence) [Computer Architecture](/research-area/computer-architecture) [Resilience and Safety](/research-area/resilience) ## External Links [IEEE Digital Library](https://ieeexplore.ieee.org/document/9151812) ## Uploaded Files [Published manuscript](https://research.nvidia.com/sites/default/files/pubs/2020-06_PyTorchFI%3A-A-Runtime//20-DSML-PyTorchFI.pdf "Open file in new window")6.38 MB ## Copyright This material is posted here with permission of the IEEE. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to .