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Record link: 
http://hdl.handle.net/20.500.12708/58066
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Title: 
Dependable Deep Learning: Towards Cost-Efficient Resilience of Deep Neural Network Accelerators against Soft Errors and Permanent Faults
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Citation: 
Hanif, M. A., & Shafique, M. (2020). Dependable Deep Learning: Towards Cost-Efficient Resilience of Deep Neural Network Accelerators against Soft Errors and Permanent Faults. In 2020 IEEE 26th International Symposium on On-Line Testing and Robust System Design (IOLTS). 2020 IEEE 26th International Symposium on On-Line Testing and Robust System Design (IOLTS), Napoli, Italy. IEEE. https://doi.org/10.1109/iolts50870.2020.9159734
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Publisher DOI: 
10.1109/iolts50870.2020.9159734
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Publication Type: 
Inproceedings - Full-Paper Contribution
en
Authors: 
Hanif, Muhammad Abdullah 
Shafique, Muhammad 
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Organisational Unit: 
E191-02 - Forschungsbereich Embedded Computing Systems 
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Published in: 
2020 IEEE 26th International Symposium on On-Line Testing and Robust System Design (IOLTS) 
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Date (published): 
2020
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Event name: 
2020 IEEE 26th International Symposium on On-Line Testing and Robust System Design (IOLTS) 
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Event date: 
13-Jul-2020 - 15-Jul-2020
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Event place: 
Napoli, Italy
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Number of Pages: 
4
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Publisher: 
IEEE
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Peer reviewed: 
Yes
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Research Areas: 
Computer Engineering and Software-Intensive Systems: 100%
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Science Branch: 
Informatik
Elektrotechnik, Elektronik, Informationstechnik
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Appears in Collections:
Conference Paper

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