StimulusRL: A Universal Deep Reinforcement Learning Stimulus Agent for Coverage-Driven Chip Design Verification
PaperFirst seen 6/18/2026
Last seen 6/18/2026
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11 connectionsThe paper reports coverage AUC as a key evaluation metric.
The paper applies coverage-driven generation methodology through the StimulusRL framework.
Jingyi Chen is listed as a co-author of the paper.
Chenyao Zhu is listed as a co-author of the paper.
The paper employs deep reinforcement learning as its core methodology.
The paper mentions DifuzzRTL as a related RTL fuzzing tool.
The paper mentions BugsBunny as a related RTL fuzzing tool.
The paper mentions GraphCov as a related coverage-driven test generation tool.
The paper introduces StimulusRL as a novel deep RL stimulus agent for coverage-driven chip verification.
The paper mentions Bayesian network approaches as early coverage-driven generation work.
The paper introduces DVSBench as a benchmark suite for evaluating DV stimulus agents.