Stimuli Redundancy Reduction for Nonlinear Functional Verification Coverage Models Using Artificial Neural Networks

As functional verification persists in being one of the most demanding and tedious tasks of SoC development, the research community continues to explore expert systems that reduce the time cost for reaching coverage closure. Some typical coverage items that are difficult to fill using Machine Learni...

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Bibliographic Details
Main Authors: Cristescu, Mihai-Corneliu, Ciupitu, Daniel
Format: Conference Proceeding
Language:English
Subjects:
Artificial Neural Network
Artificial neural networks
Automation
Costs
Coverage Closure
Expert systems
Functional Coverage
Functional Verification
Keras
Machine learning
Nonlinear Probability Coverpoint
Probability distribution
Python
Redundancy
Stimuli Redundancy Reduction
System-on-Chip
Task analysis
TensorBoard
TensorFlow
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Summary:As functional verification persists in being one of the most demanding and tedious tasks of SoC development, the research community continues to explore expert systems that reduce the time cost for reaching coverage closure. Some typical coverage items that are difficult to fill using Machine Learning inference are the coverpoints with nonlinear probability distributions, such as power-of-two values or "min & max" values. This paper presents an efficient solution based on Artificial Neural Networks that efficiently reaches coverage closure for such coverpoints. This article highlights the solution implementation, underlines the experimental results, and states suggestions for further research.
ISSN:2377-0678
DOI:10.1109/CAS52836.2021.9604141