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An Investigation on the Resilience of Long Short-Term Memory Deep Neural Networks.

Record Type:	Electronic resources : Monograph/item
Title/Author:	An Investigation on the Resilience of Long Short-Term Memory Deep Neural Networks./
Author:	Vasquez, Christopher.
Description:	1 online resource (121 pages)
Notes:	Source: Masters Abstracts International, Volume: 85-03.
Contained By:	Masters Abstracts International85-03.
Subject:	Software. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30556479click for full text (PQDT)
ISBN:	9798380263177

An Investigation on the Resilience of Long Short-Term Memory Deep Neural Networks.
Vasquez, Christopher.

An Investigation on the Resilience of Long Short-Term Memory Deep Neural Networks. - 1 online resource (121 pages)

Source: Masters Abstracts International, Volume: 85-03.

Thesis (M.Sc.)--Louisiana State University and Agricultural & Mechanical College, 2023.

Includes bibliographical references

In a world of continuously advancing technology, the reliance on these technologies continues to increase. Recently, transformer networks [22] have been implemented through various projects such as ChatGPT. These networks are extremely computationally demanding and require cutting-edge hardware to explore. However, with the growing increase and popularity of these neural networks, a question of reliability and resilience comes about, especially as the dependency and research on these networks grow. Given the computational demand of transformer networks, we investigate the resilience of the weights and biases of the predecessor of these networks, i.e. the Long Short-Term (LSTM) neural network, through four implementations of the original LSTM network. Based on the observations made through fault injection of these networks, we propose an effective means of fault mitigation through Hamming encoding of selected weights and biases in a given network and lay the groundwork for similar mitigation methods with transformers.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798380263177Subjects--Topical Terms:

619355
Software.
Index Terms--Genre/Form:

542853
Electronic books.

An Investigation on the Resilience of Long Short-Term Memory Deep Neural Networks.
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245 1 3 $a An Investigation on the Resilience of Long Short-Term Memory Deep Neural Networks.
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300 $a 1 online resource (121 pages)
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500 $a Source: Masters Abstracts International, Volume: 85-03.
500 $a Advisor: Vaidyanathan, Ramachandran.
502 $a Thesis (M.Sc.)--Louisiana State University and Agricultural & Mechanical College, 2023.
504 $a Includes bibliographical references
520 $a In a world of continuously advancing technology, the reliance on these technologies continues to increase. Recently, transformer networks [22] have been implemented through various projects such as ChatGPT. These networks are extremely computationally demanding and require cutting-edge hardware to explore. However, with the growing increase and popularity of these neural networks, a question of reliability and resilience comes about, especially as the dependency and research on these networks grow. Given the computational demand of transformer networks, we investigate the resilience of the weights and biases of the predecessor of these networks, i.e. the Long Short-Term (LSTM) neural network, through four implementations of the original LSTM network. Based on the observations made through fault injection of these networks, we propose an effective means of fault mitigation through Hamming encoding of selected weights and biases in a given network and lay the groundwork for similar mitigation methods with transformers.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2023
538 $a Mode of access: World Wide Web
650 4 $a Software. $2 gtt. $3 619355
650 4 $a Neurons. $3 588699
650 4 $a Deep learning. $3 3554982
650 4 $a Sentiment analysis. $2 lcstt $3 3266790
650 4 $a Fault tolerance. $3 3561030
650 4 $a Nuclear power plants. $3 645334
650 4 $a Neural networks. $3 677449
650 4 $a Computer science. $3 523869
650 4 $a Nuclear engineering. $3 595435
655 7 $a Electronic books. $2 lcsh $3 542853
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690 $a 0984
690 $a 0552
710 2 $a ProQuest Information and Learning Co. $3 783688
710 2 $a Louisiana State University and Agricultural & Mechanical College. $3 783779
773 0 $t Masters Abstracts International $g 85-03.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30556479 $z click for full text (PQDT)