In simulation-based functional verification, composing and debugging test benches can be tedious and time-consuming. A simulation-based data-mining approach (Wen et al., 2005) was proposed as an alternative for functional test pattern generation. However, the core of the approach is in solving Boolean learning, which is the problem of learning Boolean functions from bit-level simulation data. In this paper, an efficient data mining engine based on novel decision-diagram (DD) based learning approaches is presented. The authors compare the DD-based learning approaches to other known methods such as nearest neighbor and support vector machine. The authors show that the new Boolean data miner is efficient for practical use and the learned results can provide compact and accurately approximate representations of Boolean functions. Finally, the authors show that the proposed methodology incorporated with the current Boolean data miner can achieve high fault coverage (95.36%) on the OpenRISC 1200 microprocessor, demonstrating the effectiveness of our approach
在基于仿真的功能验证中，编写和调试测试台可能是冗长而耗时的。一种基于模拟的数据挖掘方法(Wen et al.， 2005)被提出作为功能测试模式生成的替代方法。然而，该方法的核心是解决布尔学习问题，即从位级模拟数据中学习布尔函数的问题。本文提出了一种基于决策图学习方法的高效数据挖掘引擎。作者将基于dd的学习方法与其他已知方法(如最近邻和支持向量机)进行了比较。结果表明，该算法在实际应用中是有效的，学习结果可以提供布尔函数的紧凑和精确的近似表示。最后，作者表明，将所提出的方法与当前的布尔数据挖掘器相结合，可以在OpenRISC 1200微处理器上实现高故障覆盖率(95.36%)，证明了我们方法的有效性
{"title":"Simulation Data Mining for Functional Test Pattern Justification","authors":"Charles H.-P. Wen, Li-C. Wang","doi":"10.1109/MTV.2005.24","DOIUrl":"https://doi.org/10.1109/MTV.2005.24","url":null,"abstract":"In simulation-based functional verification, composing and debugging test benches can be tedious and time-consuming. A simulation-based data-mining approach (Wen et al., 2005) was proposed as an alternative for functional test pattern generation. However, the core of the approach is in solving Boolean learning, which is the problem of learning Boolean functions from bit-level simulation data. In this paper, an efficient data mining engine based on novel decision-diagram (DD) based learning approaches is presented. The authors compare the DD-based learning approaches to other known methods such as nearest neighbor and support vector machine. The authors show that the new Boolean data miner is efficient for practical use and the learned results can provide compact and accurately approximate representations of Boolean functions. Finally, the authors show that the proposed methodology incorporated with the current Boolean data miner can achieve high fault coverage (95.36%) on the OpenRISC 1200 microprocessor, demonstrating the effectiveness of our approach","PeriodicalId":355864,"journal":{"name":"International Workshop on Microprocessor Test and Verification","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125410990","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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