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    针对可观测性语句覆盖准则的RTL激励生成

    An RT-Level Vector Generation Method for Observability-Based Statement Coverage

    • 摘要: 传统的语句覆盖准则只考虑语句的可控制性,忽略其可观测性,这可能导致表面上很高的覆盖率数据所蕴含的可信度不高.鉴于此,可观测性覆盖评估准则被提了出来.随着设计规模不断加大,该准则变得越来越重要.首先提出一种可观测性信息的表征方式以及可观测性判定规则,在此基础上,提出一种针对可观测性语句覆盖准则的RTL激励生成方法.这是一种基于模拟的方法,它以所有未观测语句的分布作为启发式信息,指导激励生成.实验结果显示,提出的方法是高效的.

       

      Abstract: Traditional statement coverage metric based on the activation of statements, without taking observability into account, can result in an artificially high reading of coverage and a false sense of confidence. So the observability-based statement coverage metric is proposed. This metric computes observability information to determine whether the effects of errors activated by the program stimuli can be observed. With the density and complexity of circuits extended, this metric plays a more and more important role during verification. Introduced in this paper is a method of vector generation for the observability-based statement coverage metric. The contribution of the work includes two aspects. Firstly, precise and concise abstract representations are presented from HDL descriptions to model observability information. Secondly, a novel simulation-based algorithm is presented to generate vectors for the observability-based statement coverage. During this procedure, the proposed algorithm always tries to cover all unobserved statements, and reduce unnecessary backtracking, so it is efficient. Finally, the method proposed has been implemented as a prototype tool for VHDL designs, and the results on benchmarks show the significant benefits.

       

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