Investigating the Impact of Radiation-Induced Soft Errors on the Reliability of Approximate Computing Systems
Abstract
Approximate Computing (AxC) is a well-known paradigm able to reduce the computational and power overheads of a multitude of applications, at the cost of a decreased accuracy. Convolutional Neural Networks (CNNs) have proven to be particularly suited for AxC because of their inherent resilience to errors. However, the implementation of AxC techniques may affect the intrinsic resilience of the application to errors induced by Single Events in a harsh environment. This work introduces an experimental study of the impact of neutron irradiation on approximate computing techniques applied on the data representation of a CNN.
Main Authors
Format
Conferences
Conference paper
Published
2020
Series
Subjects
Publication in research information system
Publisher
IEEE
The permanent address of the publication
https://urn.fi/URN:NBN:fi:jyu-202105042598Use this for linking
Parent publication ISBN
978-1-7281-9458-5
Review status
Non-peer reviewed
ISSN
1550-5774
DOI
https://doi.org/10.1109/DFT50435.2020.9250865
Conference
IEEE International Symposium on Defect and Fault Tolerance in VLSI and Nanotechnology Systems
Language
English
Published in
Proceedings : IEEE International Symposium on Defect and Fault Tolerance in VLSI Systems
Is part of publication
DFT 2020 : 33rd IEEE International Symposium on Defect and Fault Tolerance in VLSI and Nanotechnology Systems
Citation
- Matana Luza, L., Söderström, D., Tsiligiannis, G., Puchner, H., Cazzaniga, C., Sanchez, E., Bosio, A., & Dilillo, L. (2020). Investigating the Impact of Radiation-Induced Soft Errors on the Reliability of Approximate Computing Systems. In L. Dilillo, M. Psarakis, & T. Siddiqua (Eds.), DFT 2020 : 33rd IEEE International Symposium on Defect and Fault Tolerance in VLSI and Nanotechnology Systems. IEEE. Proceedings : IEEE International Symposium on Defect and Fault Tolerance in VLSI Systems, 2020. https://doi.org/10.1109/DFT50435.2020.9250865
License
Funder(s)
European Commission
Funding program(s)
MSCA Innovative Training Networks (ITN)
MSCA Innovative Training Networks (ITN)
Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Education and Culture Executive Agency (EACEA). Neither the European Union nor EACEA can be held responsible for them.
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