A Survey on Deep Learning Resilience Assessment Methodologies-Reference-Cited by-同舟云学术

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A Survey on Deep Learning Resilience Assessment Methodologies

Published:2023-02 Issue:2 Volume:56 Page:57-66
ISSN:0018-9162
Container-title:Computer
language:
Short-container-title:Computer

Author:

Ruospo Annachiara¹^ORCID,Sanchez Ernesto¹^ORCID,Luza Lucas Matana²^ORCID,Dilillo Luigi²^ORCID,Traiola Marcello³^ORCID,Bosio Alberto⁴^ORCID

Affiliation:

1. Postdoctoral Researcher, Politecnico di Torino, Turin, Italy

2. Research Engineer, LIRMM, CNRS, University of Montpellier, Montpellier, France

3. Researcher Inria centre, Rennes University, IRISA, Rennes, France

4. Full Professor, Ecole Centrale de Lyon, Institute of Nanotechnology, Lyon, France

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Computer Science

Link

http://xplorestaging.ieee.org/ielx7/2/10042075/10042090.pdf?arnumber=10042090

Reference20 articles.

1. An Emulation Platform for Evaluating the Reliability of Deep Neural Networks

2. Resiliency of automotive object detection networks on GPU architectures

3. Evaluation and Mitigation of Soft-Errors in Neural Network-Based Object Detection in Three GPU Architectures

4. Statistical fault injection: Quantified error and confidence

5. A hybrid fault injection approach based on simulation and emulation co-operation

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1. Impact of 3D learning resources on learning resilience: mediating roles of positive emotion and cognitive load;Humanities and Social Sciences Communications;2024-08-08

2. Assessment of Radiation-Induced Soft Error on Unmanned Surface Vehicles;IEEE Transactions on Nuclear Science;2024-08

3. Reliability and Security of AI Hardware;2024 IEEE European Test Symposium (ETS);2024-05-20

4. Approximate Fault-Tolerant Neural Network Systems;2024 IEEE European Test Symposium (ETS);2024-05-20

5. Special Session: Reliability Assessment Recipes for DNN Accelerators;2024 IEEE 42nd VLSI Test Symposium (VTS);2024-04-22

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