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Measures of Syntactic Complexity for Modeling Behavioral VHDL

机译:行为VHDL建模的语法复杂性度量

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Complexity measures are potentially useful in developing modeling and re-use strategies and are recognized as being useful indictors of development cost and lifecycle metrics for systems design. In this paper, a syntactic measure complexity model for VHDL descriptions is investigated. The approach leverages similarities between VHDL models and software algorithms, where syntactic modeling has been previously applied. Aspects of the measure, including observed and estimated model length, volume, syntactic information, and abstraction level are defined and discussed. As a principle result, syntactic information modeling is related to Kolmogorov intrinsic complexity as a minimum design size implementation. Experimental data on VHDL modeling and complexity measurement is presented, with potential model comprehensibility and resource estimation applications.
机译:复杂性度量在开发建模和重用策略中可能很有用,并且被认为是系统设计的开发成本和生命周期度量的有用指标。本文研究了用于VHDL描述的句法量度复杂度模型。该方法利用了先前已应用语法建模的VHDL模型与软件算法之间的相似性。定义和讨论了度量的各个方面,包括观察到的和估计的模型长度,量,语法信息和抽象级别。作为一个原则性的结果,句法信息建模与Kolmogorov固有的复杂性有关,这是最小设计尺寸的实现。提出了有关VHDL建模和复杂性测量的实验数据,以及潜在的模型可理解性和资源估计应用程序。

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