| Title | Flexible Framework for Stimuli Redundancy Reduction in Functional Verification Using Artificial Neural Networks |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Cristescu, Mihai-Corneliu, Bob, Cristian |
| Conference Name | 2021 International Symposium on Signals, Circuits and Systems (ISSCS) |
| Date Published | jul |
| Keywords | Artificial neural networks, Circuits and systems, compositionality, Metrics, pubcrawl, Redundancy, resilience, Resiliency, Scalability, scalable verification, Time to market |
| Abstract | Within the ASIC development process, the phase of functional verification is a major bottleneck that affects the product time to market. A technique that decreases the time cost for reaching functional coverage closure is reducing the stimuli redundancy during the test regressions. This paper addresses such a solution and presents a novel, efficient, and scalable implementation that harnesses the power of artificial neural networks. This article outlines the concept strategy, highlights the framework structure, lists the experimental results, and underlines future research directions. |
| DOI | 10.1109/ISSCS52333.2021.9497443 |
| Citation Key | cristescu_flexible_2021 |
Flexible Framework for Stimuli Redundancy Reduction in Functional Verification Using Artificial Neural Networks