Cross-product functional coverage analysis using machine learning clustering techniques

Mandouh, Eman El; Salem, Ashraf; Amer, Mennatallah; Wassal, Amr G.

doi:10.1109/dtis.2018.8368574

Cited by 2 publications

(2 citation statements)

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“…In [31], a two-round clustering algorithm using k-means with Jaccard similarity is used to help narrow down the number of cross coverage items to analyze. In the first round, a binary connectivity matrix encodes the associations between cover-crosses.…”

Section: Coverage Collectionmentioning

confidence: 99%

“…More recent research in the domain of stimulus and test generation used a combination of supervised and unsupervised ML models such as neural networks, random forest and support vector machines to reduce the amount of needed input iterations and testcases to reach the planned coverage goals [9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28]. In the scope of coverage collection, there are studies that show improvements in both the runtime of simulations that capture coverage and the percentage of coverage reached, when either a supervised or unsupervised ML model is used [29][30][31]. The commonly used unsupervised model is the k-means clustering algorithm and for the supervised model, the deep neural network.…”

Section: Introductionmentioning

confidence: 99%

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Survey on Machine Learning Algorithms Enhancing the Functional Verification Process

Ismail¹,

Ghany

2021

Electronics

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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.

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Section: Coverage Collectionmentioning

confidence: 99%