research-article

FTT-NAS: Discovering Fault-tolerant Convolutional Neural Architecture

Authors:

Huazhong YangAuthors Info & Claims

ACM Transactions on Design Automation of Electronic Systems (TODAES), Volume 26, Issue 6

Article No.: 44, Pages 1 - 24

https://doi.org/10.1145/3460288

Published: 12 August 2021 Publication History

Abstract

With the fast evolvement of embedded deep-learning computing systems, applications powered by deep learning are moving from the cloud to the edge. When deploying neural networks (NNs) onto the devices under complex environments, there are various types of possible faults: soft errors caused by cosmic radiation and radioactive impurities, voltage instability, aging, temperature variations, malicious attackers, and so on. Thus, the safety risk of deploying NNs is now drawing much attention. In this article, after the analysis of the possible faults in various types of NN accelerators, we formalize and implement various fault models from the algorithmic perspective. We propose Fault-Tolerant Neural Architecture Search (FT-NAS) to automatically discover convolutional neural network (CNN) architectures that are reliable to various faults in nowadays devices. Then, we incorporate fault-tolerant training (FTT) in the search process to achieve better results, which is referred to as FTT-NAS. Experiments on CIFAR-10 show that the discovered architectures outperform other manually designed baseline architectures significantly, with comparable or fewer floating-point operations (FLOPs) and parameters. Specifically, with the same fault settings, F-FTT-Net discovered under the feature fault model achieves an accuracy of 86.2% (VS. 68.1% achieved by MobileNet-V2), and W-FTT-Net discovered under the weight fault model achieves an accuracy of 69.6% (VS. 60.8% achieved by ResNet-18). By inspecting the discovered architectures, we find that the operation primitives, the weight quantization range, the capacity of the model, and the connection pattern have influences on the fault resilience capability of NN models.

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Index Terms

FTT-NAS: Discovering Fault-tolerant Convolutional Neural Architecture
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
2. Hardware
  1. Robustness
    1. Fault tolerance

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cover image ACM Transactions on Design Automation of Electronic Systems

ACM Transactions on Design Automation of Electronic Systems Volume 26, Issue 6

November 2021

218 pages

ISSN:1084-4309

EISSN:1557-7309

DOI:10.1145/3472284

Editor:
X. Sharon Hu
University of Notre Dame, USA

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Copyright © 2021 Association for Computing Machinery.

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ACM Journals for the Design of Smart and Connected Systems

Publication History

Published: 12 August 2021

Accepted: 01 April 2021

Revised: 01 March 2021

Received: 01 December 2020

Published in TODAES Volume 26, Issue 6

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National Natural Science Foundation of China
National Key R&D Program of China
Beijing National Research Center for Information Science and Technology (BNRist)
Beijing Innovation Center for Future Chips
Tsinghua University and Toyota Joint Research Center for AI Technology of Automated Vehicle
Beijing Academy of Artificial Intelligence

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https://doi.org/10.3390/rs16050894
Huang HLiu CXue XLiu BLi HLi X(2024)MRFI: An Open-Source Multiresolution Fault Injection Framework for Neural Network ProcessingIEEE Transactions on Very Large Scale Integration (VLSI) Systems10.1109/TVLSI.2024.338440432:7(1325-1335)Online publication date: Jul-2024
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Xue XLiu CWang YYang BLuo TZhang LLi HLi X(2023)Soft Error Reliability Analysis of Vision TransformersIEEE Transactions on Very Large Scale Integration (VLSI) Systems10.1109/TVLSI.2023.331713831:12(2126-2136)Online publication date: 5-Oct-2023
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Xue XLiu CLiu BHuang HWang YLuo TZhang LLi HLi X(2023)Exploring Winograd Convolution for Cost-Effective Neural Network Fault ToleranceIEEE Transactions on Very Large Scale Integration (VLSI) Systems10.1109/TVLSI.2023.330689431:11(1763-1773)Online publication date: 1-Sep-2023
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