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arXiv 1004.1697quant-phcs.ET
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基于库的可逆逻辑综合方法

A Library-Based Synthesis Methodology for Reversible Logic

Mehdi Saeedi, Mehdi Sedighi, Morteza Saheb Zamani

摘要:

本文提出了一种基于库的可逆电路综合方法,其中可逆规范被认为是由一组循环组成的置换。为此,引入了预合成优化步骤,以从不可逆函数构造可逆规范。此外,一个基于循环的表示模型被用作中间格式,在建议的综合方法。选定的中间格式用作所有潜在表示模型的焦点。为了合成给定的函数,使用包含七个构建块的库,其中每个构建块是长度小于6的循环。为了合成大的周期,我们还提出了一个分解算法,它产生所有可能的最小和不等价的因式分解为一个给定的周期长度大于5。所有分解都包含最大数量的不相交圈。生成的分解结合使用一种新的循环分配算法,提出了基于图匹配问题,以选择最佳的可能的循环对。然后,通过使用库的可用组件来合成每一对。分解算法与循环分配方法一起被认为是一种结合方法,该方法为每个循环从库中选择一个构建块。最后,一个综合后的优化步骤被引入到优化的综合结果方面的不同的成本。

英文摘要:

In this paper, a library-based synthesis methodology for reversible circuits is proposed where a reversible specification is considered as a permutation comprising a set of cycles. To this end, a pre-synthesis optimization step is introduced to construct a reversible specification from an irreversible function. In addition, a cycle-based representation model is presented to be used as an intermediate format in the proposed synthesis methodology. The selected intermediate format serves as a focal point for all potential representation models. In order to synthesize a given function, a library containing seven building blocks is used where each building block is a cycle of length less than 6. To synthesize large cycles, we also propose a decomposition algorithm which produces all possible minimal and inequivalent factorizations for a given cycle of length greater than 5. All decompositions contain the maximum number of disjoint cycles. The generated decompositions are used in conjunction with a novel cycle assignment algorithm which is proposed based on the graph matching problem to select the best possible cycle pairs. Then, each pair is synthesized by using the available components of the library. The decomposition algorithm together with the cycle assignment method are considered as a binding method which selects a building block from the library for each cycle. Finally, a post-synthesis optimization step is introduced to optimize the synthesis results in terms of different costs.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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