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Scalable trace signal selection using machine learning

A key problem in post-silicon 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 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. Our approach uses (1) bounded mock simulations to generate training vectors set for the machine learning technique, and (2) an elimination approach to identify the most profitable signals set. Experimental results indicate that our approach can improve restorability by up to 63.3% (17.2% on average) with a faster or comparable runtime.