Online Filtration of Stimuli for Microprocessor Verification

One of the most critical issues during functional verification is to generate highly effective stimuli.As verification proceeds,the effectiveness of verification stimuli decreases.To improve the effectiveness of stimuli,an online filteration technique to process is proposed to generated stimuli.This technique employs one-class support vector machines to online construct a classifier to predict whether or not a newly generated stimulus is redundant,and the predicted redundant stimulus will not be sent for simulation.Besides,we also propose an instruction sequence kernel to measure the similarities among instruction sequences.Experimental results demonstrate that this technique can reduce about 83% stimuli and 79% verification time in comparison with conventional constrained random generation.