Data Learning Methodologies for Improving the Efficiency of Constrained Random Verification

Functional verification continues to be one of the most time-consuming steps in the chip design cycle. Simulation-based verification is well practised in industry thanks to its flexibility and scalability. The completeness of the verification is measured by coverage metrics. Generating effective tests to achieve a satisfactory coverage level is a difficult task in verification. Constrained random verification is commonly used to alleviate the manual efforts for producing direct tests. However, there are yet many situations where unnecessary verification efforts in terms of simulation cycles and man hours are spent. Also, it is observed that lots of data generated in existing constrained random verification process are barely analysed, and then discarded after simplistic correctness checking. Based on our previous research on data mining and exposure to the industrial verification process, we identify that there are opportunities in extracting knowledge from the constrained random verification data and use it to improve the verification efficiency.In constrained random verification, when a simulation run of tests instantiated by a test template cannot reach the coverage goal, there are two possible reasons: insufficient simulation, and improper constraints and/or biases. There are three actions that a verification engineer can usually do to address the problem: to simulate more tests, to refine the test template, or to change to a new test template. Accordingly, we propose three data learning methodologies to help the engineers make more informed decisions in these three application scenarios and thus improve the verification efficiency.The first methodology identifies important ("novel") tests before simulation based on what have been already simulated. By only simulating those novel tests and filtering out redundant tests, tremendous resources such as simulation cycles and licenses can be saved. The second methodology extracts the unique properties from those novel tests identified in simulation and uses them to refine the test template. By leveraging the extracted knowledge, more tests similar to the novel ones are generated. And thus the new tests are more likely to activate coverage events that are otherwise difficult to hit by extensive simulation. The third methodology analyses a collection of existing test items (test templates) and identifies feasible augmentation to the test plan. By automatically adding new test items based on the data analysis, it alleviates the manual efforts for closing coverage holes.The proposed data learning methodologies were developed and applied in the setting of verifying commercial microprocessor and SoC platform designs. The experiments in this dissertation were conducted in the verification environment of a commercial microprocessor and a SoC platform in Freescale Semiconductor Inc. and were in parallel with the on-going verification efforts. The experiment results demonstrate the feasibility and effectiveness of building learning frameworks to improve verification efficiency