Clustering-based failure triage for RTL regression debugging

Regression verification at the pre-silicon stage has experienced a dramatic boost in capabilities over the past years. With the aid of assertions, improved simulation coverage and formal verification tools, a vast amount of trace data and myriads of failures are often generated after each regression run. Along these lines, modern flows face an emerging need to appropriately categorize, prioritize and distribute these failures to the engineer(s) best-suited for detailed debugging of each failure. This task is known as failure triage. Despite its resource-intensive nature, triage remains a predominantly manual process. In this work, an automated data-mining failure triage framework is introduced that mines simulation and SAT-based design debugging data, uncovers relations among verification failures and automatically groups the related ones together. The core characteristic of the framework is a novel feature-based representation for verification failures and a new multiple-pass clustering strategy that surpass previous methodologies in accuracy, robustness and flexibility. The proposed triage engine achieves an 89% average accuracy in failure categorization and compared to existing solutions, it reduces the number of misplaced verification failures by 47% on the average.