PyTorchFI: A Runtime Perturbation Tool for DNNs
PyTorchFI: A Runtime Perturbation Tool for DNNs
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PyTorchFI：DNN 的运行时扰动工具
DOI：
10.1109/dsn-w50199.2020.00014
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
S. Hari
中科院分区：
文献类型：
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作者：
Abdulrahman Mahmoud;Neeraj Aggarwal;Alex Nobbe;Jose Rodrigo Sanchez Vicarte;S. Adve;Christopher W. Fletcher;I. Frosio;S. Hari
PyTorchFI是深度神经网络（dnn）的运行时扰动工具，为流行的PyTorch深度学习平台实现。PyTorchFI使用户能够在运行时对dnn的权重或神经元执行扰动。它的设计考虑到了程序员，提供了一个简单易用的API，只需三行代码即可使用。它还提供了一个可扩展的接口，使研究人员能够从各种扰动模型中进行选择（或设计自己的自定义模型），这允许研究硬件错误（或一般扰动）传播到DNN输出的软件层。此外，PyTorchFI是非常通用的：我们展示了如何将它应用于可靠性和可靠性研究的五个不同用例，包括分类网络的弹性分析，对象检测网络的弹性分析，对对抗性攻击的模型分析，训练弹性模型，以及DNN互操作性。本文讨论了PyTorchFI的技术基础和设计决策，使其成为易于使用，可扩展，快速和通用的研究工具。PyTorchFI是开源的，可以通过pip或github下载：https://github.com/pytorchfi
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: https://github.com/pytorchfi