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An Optimized Majority Logic Synthesis Methodology for Quantum-Dot Cellular Automata

机译:量子点元胞自动机的优化多数逻辑综合方法

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Quantum-dot cellular automata (QCA) has been widely considered as a replacement candidate for complementary metal-oxide semiconductor (CMOS). The fundamental logic device in QCA is the majority gate. In this paper, we propose an efficient methodology for majority logic synthesis of arbitrary Boolean functions. We prove that our method provides a minimal majority expression and an optimal QCA layout for any given three-variable Boolean function. In order to obtain high-quality decomposed Boolean networks, we introduce a new decomposition scheme that can decompose all Boolean networks efficiently. Furthermore, our method removes all the redundancies that are produced in the process of converting a decomposed network into a majority network. In existing methods, however, these redundancies are not considered. We have built a majority logic synthesis tool based on our method and several existing logic synthesis tools. Experiments with 40 multiple-output benchmarks indicate that, compared to existing methods, 37 benchmarks are optimized by our method, up to 31.6%, 78.2%, 75.5%, and 83.3% reduction in level count, gate count, gate input count, and inverter count, respectively, is possible with the average being 4.7%, 14.5%, 13.3%, and 26.4%, respectively. We have also implemented the QCA layouts of 10 benchmarks by using our method. Results indicate that, compared to existing methods, up to 33.3%, 76.7%, and 75.5% reduction in delay, cell count, and area, respectively, is possible with the average being 8.1%, 28.9%, and 29.0%, respectively.
机译:量子点细胞自动机(QCA)已被广泛视为互补金属氧化物半导体(CMOS)的替代候选产品。 QCA中的基本逻辑器件是多数门。在本文中,我们为任意布尔函数的多数逻辑合成提出了一种有效的方法。我们证明我们的方法为任何给定的三变量布尔函数提供了最小的多数表达式和最佳的QCA布局。为了获得高质量的分解布尔网络,我们引入了一种新的分解方案,该方案可以有效地分解所有布尔网络。此外,我们的方法消除了将分解网络转换为多数网络过程中产生的所有冗余。但是,在现有方法中,没有考虑这些冗余。我们基于我们的方法和几种现有的逻辑综合工具构建了多数逻辑综合工具。对40个多输出基准进行的实验表明,与现有方法相比,通过我们的方法优化了37个基准,电平数,门数,门输入数和门数分别减少了31.6%,78.2%,75.5%和83.3%。逆变器的数量分别是可能的,平均值分别为4.7%,14.5%,13.3%和26.4%。我们还使用我们的方法实现了10个基准的QCA布局。结果表明,与现有方法相比,延迟,单元数和面积分别减少多达33.3%,76.7%和75.5%,平均分别为8.1%,28.9%和29.0%。

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