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2. Late Breaking Results: Test Selection For RTL Coverage By Unsupervised Learning From Fast Functional Simulation
 
 # Late Breaking Results: Test Selection For RTL Coverage By Unsupervised Learning From Fast Functional Simulation

  ![Publication image](/sites/default/files/styles/wide/public/default_images/default.jpeg?itok=qUFsuJCP "Publication image")

 Functional coverage closure is an important but RTL simulation intensive aspect of constrained random verification. To reduce these computational demands, we propose test selection for functional coverage via machine learning (ML) based anomaly detection in the structural coverage space of fast functional simulators. We achieve promising results on two units from a state-of-the-art production GPU design. With our approach, an up to 85\\% RTL simulation runtime reduction can be achieved when compared to baseline constrained random test selection while achieving the same RTL functional coverage.



 ## Authors



[Rongjian Liang](/person/rongjian-liang)

[Nathaniel Pinckney](/person/nathaniel-pinckney)

Yuji Chai (Harvard University)

Mark Haoxing Ren (NVIDIA)

[Brucek Khailany](/person/brucek-khailany)

 

 

 ## Publication Date



Monday, July 10, 2023

 

 ## Published in



[60th Design Automation Conference](https://www.dac.com/)

 

 ## Research Area



[Artificial Intelligence and Machine Learning ](/research-area/machine-learning-artificial-intelligence)

[Circuits and VLSI Design](/research-area/circuits)

 

 

 ## Uploaded Files



[Test Selection For RTL Coverage By Unsupervised Learning From Fast Functional Simulation](https://d1qx31qr3h6wln.cloudfront.net/publications/DACLBR_Fmod_ai_final%20%281%29.pdf "Open file in new window")3.33 MB