从仿真轨迹中提取数字设计属性的新方法

M. Hanafy, H. Said, A. Wahba
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引用次数: 4

摘要

介绍了一种从仿真轨迹中提取数字设计特性的新方法。创新的方法是基于一种新的数据挖掘技术,并以预期设计的静态分析为指导。该方法的挖掘引擎基于创新的广度优先决策树(BF-DT)搜索算法。对决策树中各节点的数据结构进行了处理,很好地呈现了输入仿真轨迹数据空间的子空间。此外,BF-DT还增加了新的特性,以提高其在输出顺序断言和搜索时间方面的性能。提出了一种新的静态分析技术,用于提取数字设计信号之间的所有组合和顺序数据依赖关系。挖掘引擎以这些数据依赖关系为指导,提取与信号相关的完整的组合和顺序设计属性,以提取其感兴趣的信号的属性。贡献了采矿技术检测位设计了不同的大小。从挖掘引擎生成的设计属性与输入模拟轨迹中覆盖的所有设计属性完全匹配。此外,生成的属性处于尽可能高的抽象级别,从而为输入数据空间提供最佳覆盖。仿真结果表明,该方法在可行的时间内提取数字设计的比特级断言具有较高的效率。下一个挑战是包含单词级断言。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
New methodology for digital design properties extraction from simulation traces
This paper introduces a new methodology for digital design properties extraction from simulation traces. The innovated methodology is based on a new data mining technique guided with static analysis of the intended design. The mining engine of the proposed methodology is based on innovated Breadth-First Decision Tree (BF-DT) search algorithm. The data structure of each node in the decision tree is handled to well present sub-space of the input simulation traces data space. Besides, new features are added to BF-DT to enhance its performance in both output sequential assertions and time of search. A new static analysis technique is innovated to extract all the combinational and sequential data dependencies between the digital design signals. The mining engine is guided with these data dependencies to extract complete combinational and sequential design properties relating signals desired to extract properties for and their cone of interest signals. The contributed mining technique has been tested for bit-level designs with different sizes. The design properties generated from the mining engine completely match with all design properties covered in the input simulation traces. Plus, the generated properties are at the highest possible level of abstraction leading to the best coverage for the input data space. The simulation results show that the proposed methodology has proven superior efficiency in extracting bit-level assertions of digital design in a feasible time. The next challenge is to include word-level assertions as well.
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