國立虎尾科技大學 |

IC Simulations for Machine Learning Based Algorithms.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	IC Simulations for Machine Learning Based Algorithms./
Author:	Moein, Hanieh.
Description:	1 online resource (101 pages)
Notes:	Source: Masters Abstracts International, Volume: 84-10.
Contained By:	Masters Abstracts International84-10.
Subject:	Electrical engineering. -
Online resource:	click for full text (PQDT)
ISBN:	9798379426620

IC Simulations for Machine Learning Based Algorithms.
Moein, Hanieh.

IC Simulations for Machine Learning Based Algorithms. - 1 online resource (101 pages)

Source: Masters Abstracts International, Volume: 84-10.

Thesis (M.S.)--San Diego State University, 2023.

Includes bibliographical references

Understanding and predicting the degradation that occurs in embedded nanoscale circuits or integrated circuits (ICs) due to aging has become a vital task as it directly relates to the reliability of the device. While an IC is under performance, over time, there are several factors (e.g., temperature and workload) that affect its structure and cause degradation in various aspects of its performance like the propagation delay. This ultimately can lead to the failure of the device. There are solutions offered where one can monitor the performance and the degradation of the IC in real time to predict the deviations from its intended behavior or future failures due to the aging of the IC. The previous methods cover a single operating variable and its effect on the performance and the age of the IC. This paper covers solutions in providing dataset using simulations on HSPICE. The datasets can also be used for dynamic real-time prediction algorithms which are not specific to a single variable/condition at a time. The dynamic algorithm enables the user to apply various operating conditions and observe the effect on the age of the IC.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798379426620Subjects--Topical Terms:

596380
Electrical engineering.
Subjects--Index Terms:

Machine learningIndex Terms--Genre/Form:

554714
Electronic books.

IC Simulations for Machine Learning Based Algorithms.
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500 $a Advisor: Huang, Ke.
502 $a Thesis (M.S.)--San Diego State University, 2023.
504 $a Includes bibliographical references
520 $a Understanding and predicting the degradation that occurs in embedded nanoscale circuits or integrated circuits (ICs) due to aging has become a vital task as it directly relates to the reliability of the device. While an IC is under performance, over time, there are several factors (e.g., temperature and workload) that affect its structure and cause degradation in various aspects of its performance like the propagation delay. This ultimately can lead to the failure of the device. There are solutions offered where one can monitor the performance and the degradation of the IC in real time to predict the deviations from its intended behavior or future failures due to the aging of the IC. The previous methods cover a single operating variable and its effect on the performance and the age of the IC. This paper covers solutions in providing dataset using simulations on HSPICE. The datasets can also be used for dynamic real-time prediction algorithms which are not specific to a single variable/condition at a time. The dynamic algorithm enables the user to apply various operating conditions and observe the effect on the age of the IC.
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650 4 $a Computer engineering. $3 569006
653 $a Machine learning
653 $a Algorithms
653 $a Integrated circuits
653 $a Nanoscale circuits
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