Optimization of Constrained Random Verification using Machine Learning

University of Minnesota M.S.E.E. thesis. 2018. Major: Electrical Engineering. Advisor: Gerald Sobelman. 1 computer file (PDF); 69 pages.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