On-the-Fly Reduction of Stimuli for Functional Verification
Qi Guo, Tianshi Chen, Haihua Shen, Yunji Chen, Weiwu Hu · 2010
As a primary method for functional verification of microprocessors, simulation-based verification has received extensive studies over the last decade. Most investigations have been dedicated to the generation of stimuli (test cases), while relatively few has focused on explicitly reducing the redundant stimuli among the generated ones. In this paper, we propose an on-the-fly approach for reducing the stimuli redundancy based on machine learning techniques, which can learn from new knowledge in every cycle of simulation-based verification. Our approach can be easily embedded in traditional framework of simulation-based functional verification, and the experiments on an industrial microprocessor have validated that the approach is effective and efficient.