MRFI: An Open Source Multi-Resolution Fault Injection Framework for
Neural Network Processing
- URL: http://arxiv.org/abs/2306.11758v2
- Date: Thu, 21 Dec 2023 08:31:45 GMT
- Title: MRFI: An Open Source Multi-Resolution Fault Injection Framework for
Neural Network Processing
- Authors: Haitong Huang, Cheng Liu, Bo Liu, Xinghua Xue, Huawei Li, Xiaowei Li
- Abstract summary: MRFI is a highly multi-resolution fault injection tool for deep neural networks.
It integrates extensive fault analysis functionalities from different perspectives.
It does not modify the major neural network computing framework of PyTorch.
- Score: 8.871260896931211
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: To ensure resilient neural network processing on even unreliable hardware,
comprehensive reliability analysis against various hardware faults is generally
required before the deep neural network models are deployed, and efficient
error injection tools are highly demanded. However, most existing fault
injection tools remain rather limited to basic fault injection to neurons and
fail to provide fine-grained vulnerability analysis capability. In addition,
many of the fault injection tools still need to change the neural network
models and make the fault injection closely coupled with normal neural network
processing, which further complicates the use of the fault injection tools and
slows down the fault simulation. In this work, we propose MRFI, a highly
configurable multi-resolution fault injection tool for deep neural networks. It
enables users to modify an independent fault configuration file rather than
neural network models for the fault injection and vulnerability analysis.
Particularly, it integrates extensive fault analysis functionalities from
different perspectives and enables multi-resolution investigation of the
vulnerability of neural networks. In addition, it does not modify the major
neural network computing framework of PyTorch. Hence, it allows parallel
processing on GPUs naturally and exhibits fast fault simulation according to
our experiments.
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