@inproceedings{3e77d50ae0054d688b8c40a02d3751c6,
title = "Evaluating the Impact of Fault-Tolerance Capability of Deep Neural Networks Caused by Faults",
abstract = "Deep neural network (DNN) is one effective technique used for the artificial intelligence applications. A DNN consists of a large amount of neurons arranged in a form of multilayers. Typically, a DNN has over-provisioning neurons such that it has inherent fault-tolerance capability. However, how to evaluate the fault-tolerance capability of DNNs is a question. In this paper, we propose a simulator to estimate the loss of inference accuracy due to the faults in a DNN model or the memory faults in hardware accelerator. The simulator is implemented based on the platforms of Keras and Tensorflow. It can evaluate the fault-tolerance capability of a DNN at model and hardware levels. ",
keywords = "Deep neural network, convolutional neural newtork, fault tolerance, simulator",
author = "Tsai, {Yung Yu} and Li, {Jin Fu}",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 34th IEEE International System-on-Chip Conference, SOCC 2021 ; Conference date: 14-09-2021 Through 17-09-2021",
year = "2021",
doi = "10.1109/SOCC52499.2021.9739383",
language = "???core.languages.en_GB???",
series = "International System on Chip Conference",
publisher = "IEEE Computer Society",
pages = "272--277",
editor = "Gang Qu and Jinjun Xiong and Danella Zhao and Venki Muthukumar and Reza, {Md Farhadur} and Ramalingam Sridhar",
booktitle = "Proceedings - 34th IEEE International System-on-Chip Conference, SOCC 2021",
}