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Analysis and Improv...
Analysis and Improvement of Resilience for Long Short-Term Memory Neural Networks
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- Ahmadilivani, M. H. (author)
- Tallinn University of Technology, Tallinn, Estonia
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- Raik, J. (author)
- Tallinn University of Technology, Tallinn, Estonia
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- Daneshtalab, Masoud (author)
- Mälardalens universitet,Inbyggda system,Tallinn University of Technology, Tallinn, Estonia
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- Kuusik, A. (author)
- Tallinn University of Technology, Tallinn, Estonia
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(creator_code:org_t)
- Institute of Electrical and Electronics Engineers Inc. 2023
- 2023
- English.
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In: Proc. IEEE Int. Symp. Defect Fault Toler. VLSI Nanotechnol. Syst., DFT. - : Institute of Electrical and Electronics Engineers Inc.. - 9798350315004
- Related links:
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Subject headings
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- 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.
Subject headings
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering (hsv//eng)
Keyword
- 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
Publication and Content Type
- ref (subject category)
- kon (subject category)
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