Knowledge-Guided Methodology for Third-Party Soft IP Analysis

In System-on-Chip designs, third party IP reuse is prevalent as it increases productivity and reduces time-to-market. These IPs can be classified as untrusted designs since the user has no insight into IP verification or quality control process. In practice, it is generally assumed that the IP has been functionally validated by developers and thorough verification at user end is not performed. In the current state-of-the-art, lint tools are primarily used to determine IP design quality. These tools pinpoint design issues by performing static analysis of RTL code but have a limitation that they do not perform behavioral analysis. In this paper, we present a knowledge-guided methodology, which identifies RTL behavior by finding correspondences with a knowledge base of previously analysed trusted designs. In comparison to existing techniques, our approach uses combination of static and dynamic analysis techniques to better approximate design behavior. We tested our methodology by analysing several IEEE-754 floating point soft IPs. We define identification coverage and confidence factor metric to quantify our IP analysis results.