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Energy-aware and fault-tolerant custom topology design method for network-on-chips

机译:片上网络的能量感知和容错定制拓扑设计方法

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The rapid reduction in integrated circuit dimensions makes it possible to place more components on a single chip in each generation. While the increase in components requires a better communication mechanism than traditional wiring-based communication methods, new design algorithms are needed to tolerate increasing permanent failures due to shrinking technology dimensions. To solve the former problem, the network-on-chip (NoC) paradigm was developed to keep pace with the communication demands on these very large systems. This article deals with the latter problem. That is, we provide a fault-tolerant topology generation method that can tolerate single permanent link failure on a NoC architecture that is designed for a particular application. Our generated topologies provide fault-tolerance by providing at least two alternative paths between the application's communicating nodes. Our method is a genetic algorithm based method, which generates an initial population based on ring topology and produces better irregular topologies in terms of energy consumption through genetic operators. The objective function of the proposed method is to minimize the energy consumption resulting from network communication. We tested our method on several multimedia benchmarks and custom generated graphs and compared it with previous algorithms and the ring topology. Our results show that as the number of application nodes increases, our method achieves better results in a shorter time than the previous method. (C) 2018 Elsevier B.V. All rights reserved.
机译:集成电路尺寸的迅速减小使得有可能在每一代中在单个芯片上放置更多的组件。尽管组件的增加需要比传统的基于布线的通信方法更好的通信机制,但仍需要新的设计算法来承受由于技术尺寸缩小而导致的永久性故障增加。为了解决前一个问题,开发了片上网络(NoC)范式以适应这些超大型系统的通信需求。本文处理后一个问题。也就是说,我们提供了一种容错拓扑生成方法,该方法可以容忍针对特定应用程序设计的NoC架构上的单个永久链路故障。我们生成的拓扑通过在应用程序的通信节点之间提供至少两个备用路径来提供容错能力。我们的方法是一种基于遗传算法的方法,该方法基于环形拓扑生成初始种群,并通过遗传算子在能耗方面产生更好的不规则拓扑。所提出的方法的目标功能是最小化由网络通信引起的能量消耗。我们在几种多媒体基准和自定义生成的图形上测试了我们的方法,并将其与以前的算法和环形拓扑进行了比较。我们的结果表明,随着应用程序节点数量的增加,我们的方法比以前的方法在较短的时间内可获得更好的结果。 (C)2018 Elsevier B.V.保留所有权利。

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