Optimization of Constrained Random Verification using Machine Learning
2018-05
Loading...
View/Download File
Persistent link to this item
Statistics
View StatisticsJournal Title
Journal ISSN
Volume Title
Title
Optimization of Constrained Random Verification using Machine Learning
Authors
Published Date
2018-05
Publisher
Type
Thesis or Dissertation
Abstract
Constrained random simulations play a critical role in Design Verification today. But the effort and time spent to manually update the input constraints, analyzing and prioritizing the unverified features in the design, significantly affect the time taken to converge to the coverage goal. This research work focuses on the optimization of constrained random verification using Machine Learning algorithms, in a coverage-driven simulation using a Universal Verification Methodology (UVM) framework. The optimization will greatly reduce the time a simulation takes to converge to the coverage goal. This research work targets automating the update of the constraints during runtime, abstracting the need for understanding the design to verify it, using Machine Learning. The verification environment is further optimized using techniques including Objective Function, Rewinding and Dynamic Seed Manipulation. The enhanced environment resolves the limitations of the previous efforts at employing these techniques, optimizing the scalability of the environment and enhancing its compatibility at verifying complex combinational designs and sequential designs including Finite State Machines (FSMs). The optimized verification environment comprises of a SystemVerilog testbench which interfaces and interacts with a TCL environment. The methodology has been empirically demonstrated, with remarkable results showing its superior quality in terms of faster automated coverage closure, efficient final stimulus solution and proposed higher quality of coverage. Multiple Machine Learning algorithms, including a Linear Regression Model and Artificial Neural Networks, have been employed to scale the compatibility of the verification environment, making it capable of autonomously verifying designs of varied behavior. Adequate simulation results to demonstrate the same have been presented in the report.
Description
University of Minnesota M.S.E.E. thesis. 2018. Major: Electrical Engineering. Advisor: Gerald Sobelman. 1 computer file (PDF); 69 pages.
Related to
Replaces
License
Series/Report Number
Funding information
Isbn identifier
Doi identifier
Previously Published Citation
Suggested citation
Ambalakkat, Sarath Mohan. (2018). Optimization of Constrained Random Verification using Machine Learning. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/198982.
Content distributed via the University Digital Conservancy may be subject to additional license and use restrictions applied by the depositor. By using these files, users agree to the Terms of Use. Materials in the UDC may contain content that is disturbing and/or harmful. For more information, please see our statement on harmful content in digital repositories.