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Analysis and Improvement of Resilience for Long Short-Term Memory Neural Networks
Tallinn University of Technology, Tallinn, Estonia.
Tallinn University of Technology, Tallinn, Estonia.
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems. Tallinn University of Technology, Tallinn, Estonia.
Tallinn University of Technology, Tallinn, Estonia.
2023 (English)In: Proc. IEEE Int. Symp. Defect Fault Toler. VLSI Nanotechnol. Syst., DFT, Institute of Electrical and Electronics Engineers Inc. , 2023Conference paper, Published paper (Refereed)
Abstract [en]

The reliability of Artificial Neural Networks (ANNs) has emerged as a prominent research topic due to their increasing utilization in safety-critical applications. Long Short-Term Memory (LSTM) ANNs have demonstrated significant advantages in healthcare applications, primarily attributed to their robust processing of time-series data and memory-facilitated capabilities. This paper, for the first time, presents a comprehensive and fine-grain analysis of the resilience of LSTM-based ANNs in the context of gait analysis using fault injection into weights. Additionally, we improve their resilience by replacing faulty weights with zero, enabling ANNs to withstand environments that are up to 20 times harsher while experiencing up to 7 times fewer critical faults than an unprotected ANN.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc. , 2023.
Keywords [en]
Brain, Safety engineering, Fault injection, Fine-grain analysis, Health care application, Neural-networks, Research topics, Robust processing, Safety critical applications, Time-series data, Long short-term memory
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:mdh:diva-65145DOI: 10.1109/DFT59622.2023.10313559Scopus ID: 2-s2.0-85179010250ISBN: 9798350315004 (print)OAI: oai:DiVA.org:mdh-65145DiVA, id: diva2:1821876
Conference
Proceedings - IEEE International Symposium on Defect and Fault Tolerance in VLSI and Nanotechnology Systems, DFT
Available from: 2023-12-21 Created: 2023-12-21 Last updated: 2023-12-21Bibliographically approved

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Daneshtalab, Masoud

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Citation style
  • apa
  • ieee
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  • de-DE
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  • nn-NO
  • nn-NB
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Output format
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