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Extracting Logic Circuit Structure from Conjunctive Normal Form Descriptions

机译:从联合正常形式描述中提取逻辑电路结构

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Boolean Satisfiability is seeing increasing use as a decision procedure in Electronic Design Automation (EDA) and other domains. Most applications encode their domain specific constraints in Conjunctive Normal Form (CNF), which is accepted as input by most efficient contemporary SAT solvers [1-3]. However, such translation may have information loss. For example, when a circuit is encoded into CNF, structural information such as gate orientation, logic paths, signal observability, etc. is lost. However, recent research [4-6] shows that a substantial amount of the lost information can be restored in circuit form. This paper presents an efficient algorithm (CNF2CKT) for extracting circuit information from CNF instances. CNF2CKT is optimal in the sense that it extracts a maximum acyclic combinational circuit from any given CNF using the logic gates pre-specified in a library. The extracted circuit structure is valuable in various ways, particularly when the CNF is not encoded from the circuit, or the circuit description is not readily available. As an example, we show that the extracted circuit structure can be used to derive Circuit Observability Don't Cares [7] for speeding up CNF-SAT [8].
机译:布尔可满足性正在看到随着电子设计自动化(EDA)和其他域中的决策程序的越来越多。大多数应用程序以联合常规形式(CNF)编码其域特定约束,其被最有效的当代SAT溶剂(1-3)接受为输入。但是,这种翻译可能有信息丢失。例如,当电路被编码到CNF中时,诸如栅极方向,逻辑路径,信号可观察性等的结构信息丢失。然而,最近的研究[4-6]表明,可以以电路形式恢复大量丢失的信息。本文介绍了一种用于从CNF实例中提取电路信息的有效算法(CNF2CKT)。 CNF2CKT在意义上是最佳的,即它使用库中预先指定的逻辑门来从任何给定的CNF中提取最大无循环组合电路。提取的电路结构以各种方式有价值,特别是当CNF未从电路编码时,或者电路描述不容易获得。例如,我们表明提取的电路结构可用于导出电路可观察性不要关心[7]加速CNF-SAT [8]。

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