Constrained Stimulus Generation with Self-Adjusting Using Tabu Search with Memory

  • ZHAO Yanni
    EDA lab, Department of Computer Science and Technology, Tsinghua University
  • BIAN Jinian
    EDA lab, Department of Computer Science and Technology, Tsinghua University
  • DENG Shujun
    EDA lab, Department of Computer Science and Technology, Tsinghua University
  • KONG Zhiqiu
    EDA lab, Department of Computer Science and Technology, Tsinghua University
  • ZHAO Kang
    EDA lab, Department of Computer Science and Technology, Tsinghua University

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Despite the growing research effort in formal verification, industrial verification often relies on the constrained random simulation methodology, which is supported by constraint solvers as the stimulus generator integrated within simulator, especially for the large design with complex constraints nowadays. These stimulus generators need to be fast and well-distributed to maintain simulation performance. In this paper, we propose a dynamic method to guide stimulus generation by SAT solvers. An adjusting strategy named Tabu Search with Memory (TSwM) is integrated in the stimulus generator for the search and prune processes along with the constraint solver. Experimental results show that the method proposed in this paper could generate well-distributed stimuli with good performance.

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