HFMV: Hybridizing Formal Methods and Machine Learning for Verification of Analog and Mixed-Signal Circuits

  • Authors:
    Qingran Zheng (Texas A&M), Hanbin Hu (Texas A&M), Ya Wang (Texas A&M), Peng Li (Texas A&M)
    Publication ID:
    Publication Type:
    Received Date:
    Last Edit Date:
    2712.004 (Texas A&M Engineering Experiment Station)


With increasing design complexity and reliability requirements, analog and mixed-signal (AMS) verification manifests itself as a key bottleneck. While formal methods and machine learning have been proposed for AMS verification, these two types of techniques suffer from their own limitations, with the former being specifically limited by scalability and the latter by inherent errors in learning-based models. We present a new direction in AMS verification by proposing a hybrid formal/machine-learning-based verification technique (HFMV) to combine the best of the two worlds. HFMV builds formalism on the top of a machine learning model to verify AMS circuits efficiently while meeting a user-specified confidence level. Guided by formal checks, HFMV intelligently explores the high-dimensional parameter space of a given design by iteratively improving the machine learning model. As a result, it leads to accurate failure prediction in the case of a failing circuit, or a reliable pass decision in the case of a good circuit. Our experimental results demonstrate that the proposed HFMV approach is capable of identifying hard-to-find failures which are completely missed by a huge number of random simulation samples while significantly cutting down training sample size and verification cycle time.

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