Analyzing the reliability of convolutional neural networks on GPUs : GoogLeNet as a case study

Analyzing the reliability of convolutional neural networks on GPUs : GoogLeNet as a case study

Convolutional Neural Networks (CNNs) are used for tasks such as object recognition. Once a CNN model is used in a radiative environment, reliability of the system against soft errors is a crucial issue, especially in safety-critical and high-performance applications that bound with real-time respons...

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Bibliographic Details
Main Authors: Ibrahim, Younis Mohammed, Wang, Haibin, Khalid, Adam
Format: Conference or Workshop Item
Language:English
English
Published: Institute of Electrical and Electronics Engineers Inc. 2020
Subjects:
T Technology (General)
TA Engineering (General). Civil engineering (General)
TK Electrical engineering. Electronics Nuclear engineering
Online Access:http://umpir.ump.edu.my/id/eprint/42444/
http://umpir.ump.edu.my/id/eprint/42444/1/Analyzing%20the%20reliability%20of%20convolutional%20neural%20networks%20on%20GPUs.pdf
http://umpir.ump.edu.my/id/eprint/42444/2/Analyzing%20the%20reliability%20of%20convolutional%20neural%20networks%20on%20GPUs_GoogLeNet%20as%20a%20case%20study_ABS.pdf

Internet

http://umpir.ump.edu.my/id/eprint/42444/
http://umpir.ump.edu.my/id/eprint/42444/1/Analyzing%20the%20reliability%20of%20convolutional%20neural%20networks%20on%20GPUs.pdf
http://umpir.ump.edu.my/id/eprint/42444/2/Analyzing%20the%20reliability%20of%20convolutional%20neural%20networks%20on%20GPUs_GoogLeNet%20as%20a%20case%20study_ABS.pdf

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