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Joint defect- and variation-aware logic mapping of multi-outputs crossbar-based nanoarchitectures

机译:多输出基于交叉开关的纳米体系结构的联合缺陷和变异感知逻辑映射

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摘要

Nanotechnology-based manufacturing, relying on self-assembly of nanotubes or nanowires, has shown promising potentials for future nanoscale circuit designs. However, high defect density and extreme process variations for crossbar-based nanoarchitectures are expected to be fundamental design challenges. Consequently, defect and variation issues must be considered in logic mapping on nanoscale crossbars. In this paper, we establish a mathematical model for the simultaneous variation and defect-aware logic mapping of multi-outputs crossbar arrays. We model this problem using a new sub-weighted-graph isomorphism problem and propose a greedy algorithm for the variation- and defect-aware logic mapping. Based on Monte-Carlo simulation, we compare the proposed technique with other logic mapping techniques such as, variation-unaware and exhaustive search mapping in terms of accuracy as well as runtime. Results show that the effectiveness of our new mapping technique in variation and defect tolerance as well as run time improvement.
机译:依靠纳米管或纳米线的自组装,基于纳米技术的制造已显示出未来纳米级电路设计的潜力。然而,基于交叉开关的纳米架构的高缺陷密度和极端的工艺变化有望成为基本的设计挑战。因此,在纳米级交叉开关的逻辑映射中必须考虑缺陷和变化问题。在本文中,我们建立了用于多输出交叉开关阵列的同时变化和缺陷感知逻辑映射的数学模型。我们使用新的子加权图同构问题对该问题进行建模,并提出了一种用于感知变化和缺陷的逻辑映射的贪婪算法。基于Monte-Carlo仿真,我们在准确性和运行时间方面将所提出的技术与其他逻辑映射技术(例如,无变化感知和穷举搜索映射)进行了比较。结果表明,我们的新映射技术在变化和缺陷容忍以及运行时间改进方面的有效性。

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