Survey on Machine Learning Algorithms Enhancing the Functional Verification Process

The continuing increase in functional requirements of modern hardware designs means the traditional functional verification process becomes inefficient in meeting the time-to-market goal with sufficient level of confidence in the design. Therefore, the need for enhancing the process is evident. Mach...

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Autores principales:	Khaled A. Ismail, Mohamed A. Abd El Ghany
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	automation of verification functional verification machine learning coverage driven verification Electronics TK7800-8360
Acceso en línea:	https://doaj.org/article/81d1b148a6ed46dabd7b5790efb07454
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id	oai:doaj.org-article:81d1b148a6ed46dabd7b5790efb07454
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spelling	oai:doaj.org-article:81d1b148a6ed46dabd7b5790efb074542021-11-11T15:40:47ZSurvey on Machine Learning Algorithms Enhancing the Functional Verification Process10.3390/electronics102126882079-9292https://doaj.org/article/81d1b148a6ed46dabd7b5790efb074542021-11-01T00:00:00Zhttps://www.mdpi.com/2079-9292/10/21/2688https://doaj.org/toc/2079-9292The continuing increase in functional requirements of modern hardware designs means the traditional functional verification process becomes inefficient in meeting the time-to-market goal with sufficient level of confidence in the design. Therefore, the need for enhancing the process is evident. Machine learning (ML) models proved to be valuable for automating major parts of the process, which have typically occupied the bandwidth of engineers; diverting them from adding new coverage metrics to make the designs more robust. Current research of deploying different (ML) models prove to be promising in areas such as stimulus constraining, test generation, coverage collection and bug detection and localization. An example of deploying artificial neural network (ANN) in test generation shows 24.5× speed up in functionally verifying a dual-core RISC processor specification. Another study demonstrates how k-means clustering can reduce redundancy of simulation trace dump of an AHB-to-WHISHBONE bridge by 21%, thus reducing the debugging effort by not having to inspect unnecessary waveforms. The surveyed work demonstrates a comprehensive overview of current (ML) models enhancing the functional verification process from which an insight of promising future research areas is inferred.Khaled A. IsmailMohamed A. Abd El GhanyMDPI AGarticleautomation of verificationfunctional verificationmachine learningcoverage driven verificationElectronicsTK7800-8360ENElectronics, Vol 10, Iss 2688, p 2688 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	automation of verification functional verification machine learning coverage driven verification Electronics TK7800-8360
spellingShingle	automation of verification functional verification machine learning coverage driven verification Electronics TK7800-8360 Khaled A. Ismail Mohamed A. Abd El Ghany Survey on Machine Learning Algorithms Enhancing the Functional Verification Process
description	The continuing increase in functional requirements of modern hardware designs means the traditional functional verification process becomes inefficient in meeting the time-to-market goal with sufficient level of confidence in the design. Therefore, the need for enhancing the process is evident. Machine learning (ML) models proved to be valuable for automating major parts of the process, which have typically occupied the bandwidth of engineers; diverting them from adding new coverage metrics to make the designs more robust. Current research of deploying different (ML) models prove to be promising in areas such as stimulus constraining, test generation, coverage collection and bug detection and localization. An example of deploying artificial neural network (ANN) in test generation shows 24.5× speed up in functionally verifying a dual-core RISC processor specification. Another study demonstrates how k-means clustering can reduce redundancy of simulation trace dump of an AHB-to-WHISHBONE bridge by 21%, thus reducing the debugging effort by not having to inspect unnecessary waveforms. The surveyed work demonstrates a comprehensive overview of current (ML) models enhancing the functional verification process from which an insight of promising future research areas is inferred.
format	article
author	Khaled A. Ismail Mohamed A. Abd El Ghany
author_facet	Khaled A. Ismail Mohamed A. Abd El Ghany
author_sort	Khaled A. Ismail
title	Survey on Machine Learning Algorithms Enhancing the Functional Verification Process
title_short	Survey on Machine Learning Algorithms Enhancing the Functional Verification Process
title_full	Survey on Machine Learning Algorithms Enhancing the Functional Verification Process
title_fullStr	Survey on Machine Learning Algorithms Enhancing the Functional Verification Process
title_full_unstemmed	Survey on Machine Learning Algorithms Enhancing the Functional Verification Process
title_sort	survey on machine learning algorithms enhancing the functional verification process
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/81d1b148a6ed46dabd7b5790efb07454
work_keys_str_mv	AT khaledaismail surveyonmachinelearningalgorithmsenhancingthefunctionalverificationprocess AT mohamedaabdelghany surveyonmachinelearningalgorithmsenhancingthefunctionalverificationprocess
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Survey on Machine Learning Algorithms Enhancing the Functional Verification Process

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