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Survey on Machine Learning Algorithms Enhancing the Functional Verification Process

Ismail, Khaled A. ; Ghany, Mohamed A. Abd El (2022):
Survey on Machine Learning Algorithms Enhancing the Functional Verification Process. (Publisher's Version)
In: Electronics, 10 (21), MDPI, e-ISSN 2079-9292,
DOI: 10.26083/tuprints-00020072,

Available under: CC BY 4.0 International - Creative Commons, Attribution.

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Item Type: Article
Origin: Secondary publication DeepGreen
Status: Publisher's Version
Title: Survey on Machine Learning Algorithms Enhancing the Functional Verification Process
Language: English

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.

Journal or Publication Title: Electronics
Volume of the journal: 10
Issue Number: 21
Publisher: MDPI
Collation: 24 Seiten
Uncontrolled Keywords: automation of verification, functional verification, machine learning, coverage driven verification
Classification DDC: 600 Technik, Medizin, angewandte Wissenschaften > 600 Technik
600 Technik, Medizin, angewandte Wissenschaften > 620 Ingenieurwissenschaften
Divisions: 18 Department of Electrical Engineering and Information Technology > Institute of Computer Engineering > Integrated Electronic Systems (IES)
Date Deposited: 29 Apr 2022 08:53
Last Modified: 29 Apr 2022 08:53
DOI: 10.26083/tuprints-00020072
Corresponding Links:
URN: urn:nbn:de:tuda-tuprints-200728
SWORD Depositor: Deep Green
URI: https://tuprints.ulb.tu-darmstadt.de/id/eprint/20072
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