An end-to-end automatic methodology to accelerate the accuracy evaluation of deep neural networks under hardware transient faults

Jiajia JIAO; Ran WEN; Hong YANG

doi:10.1631/FITEE.2400547

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An end-to-end automatic methodology to accelerate the accuracy evaluation of deep neural networks under hardware transient faults

Regular Papers | Updated：2025-07-28

- An end-to-end automatic methodology to accelerate the accuracy evaluation of deep neural networks under hardware transient faults
  Enhanced Publication
- 加速深度神经网络在硬件瞬态故障下准确性评估的端到端自动化方法
- “In the field of deep neural networks (DNNs), hardware transient faults significantly impact safety-critical applications. Expert researchers have designed an automatic methodology, A-Mean, which uses silent data corruption rate and static two-level mean calculation to rapidly estimate classification accuracy and safety-critical misclassification. This easy-to-use tool achieves up to 922.80 times speedup compared to state-of-the-art methods, with minimal loss in safety and accuracy.”
- Frontiers of Information Technology & Electronic Engineering Vol. 26, Issue 7, Pages: 1099-1114(2025)
- Affiliations：
  
  College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China
- Author bio：
  
  ‡ Corresponding author
- Funds：
- DOI：10.1631/FITEE.2400547
  CLC： TP391
- Received：26 June 2024，
  
  Revised：2024-09-18，
  
  Published：2025-07
- Accepted：
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Jiajia JIAO, Ran WEN, Hong YANG. An end-to-end automatic methodology to accelerate the accuracy evaluation of deep neural networks under hardware transient faults[J]. Frontiers of Information Technology & Electronic Engineering, 2025, 26(7): 1099-1114.
DOI：

Jiajia JIAO, Ran WEN, Hong YANG. An end-to-end automatic methodology to accelerate the accuracy evaluation of deep neural networks under hardware transient faults[J]. Frontiers of Information Technology & Electronic Engineering, 2025, 26(7): 1099-1114. DOI： 10.1631/FITEE.2400547.

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An end-to-end automatic methodology to accelerate the accuracy evaluation of deep neural networks under hardware transient faults

DOI：10.1631/FITEE.2400547