Increasing the Efficiency of Simulation-Based Functional Verification Through Unsupervised Support Vector Analysis

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Abstract

Success of simulation-based functional verification depends on the quality and diversity of the verification tests that are simulated. The objective of test generation methods is to generate tests that exercise as much different functionality of the hardware designs as possible. In this paper, we propose a novel methodology that generates a model of the verification tests in a given test set using unsupervised support vector analysis. One potential application is to use this model to select tests that are likely to exercise functionality that has not been tested so far. Since this selection can be done before simulation, it can be used to filter redundant tests and reduce required simulation cycles. Our methodology can be combined with a test generation method like constrained-random test generation to increase its effectiveness without making fundamental changes to the verification flow. Experimental results based on application of the proposed methodology to the OpenSparc T1 processor are reported to demonstrate the practicality of our approach.