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Multi-output majority gate-based design optimization by using evolutionary algorithm

机译:基于进化算法的基于多输出多数门的设计优化

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摘要

In this paper, a novel efficient method for optimizing multi-output majority gate based designs is proposed. Majority gate is a fundamental Boolean operator in some nano-scale technologies such as quantum-dot cellular automata (QCA). As a result, the design optimization must be directly implemented on majority gates instead of optimizing the design for AND-OR gates. In some other nanotechnologies, a fundamental element is Minority gate which could be simply converted to majority gate by the De Morgan's theorem. Here, the proposed optimization method works on the basis of evolutionary computation and can reduce both the number of majority gates and the worst-case delay of the circuit. The method is compared to some other optimization algorithms and its efficiency is verified.
机译:本文提出了一种基于多输出多数门的优化设计方法。在某些纳米级技术(例如量子点细胞自动机(QCA))中,多数门是布尔运算符。结果,必须直接在多数门上执行设计优化,而不是对AND-OR门进行优化设计。在其他一些纳米技术中,基本要素是少数派门,可以通过De Morgan定理简单地转换为多数门。在此,所提出的优化方法在进化计算的基础上工作,并且可以减少多数门的数量和电路的最坏情况下的延迟。该方法与其他一些优化算法进行了比较,并验证了其效率。

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