Postsilicon Trace Signal Selection Using Machine Learning Techniques

A key problem in postsilicon validation is to identify a small set of traceable signals that are effective for debug during silicon execution. Structural analysis used by traditional signal selection techniques leads to a poor restoration quality. In contrast, simulation-based selection techniques provide superior restorability but incur significant computation overhead. In this paper, we propose an efficient signal selection technique using machine learning to take advantage of simulation-based signal selection while significantly reducing the simulation overhead. The basic idea is to train a machine learning framework with a few simulation runs and utilize its effective prediction capability (instead of expensive simulation) to identify beneficial trace signals. Specifically, our approach uses: 1) bounded mock simulations to generate training vectors for the machine learning technique and 2) a compound search-space exploration approach to identify the most profitable signals. Experimental results indicate that our approach can improve restorability by up to 143.1% (29.2% on average) while maintaining or improving runtime compared with the state-of-the-art signal selection techniques.