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

机译:网络上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:集成电路尺寸的快速降低使得可以在每个代芯片上放置更多组件。虽然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,但是需要新的设计算法来承受由于技术尺寸收缩而导致的永久性故障。为了解决前一个问题,开发了芯片上网(NOC)范式以跟上这些非常大型系统的通信需求。本文涉及后一种问题。也就是说,我们提供了一种容错拓扑生成方法,可以容忍为特定应用设计的NoC架构上的单个永久链路故障。我们所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替代路径提供容错。我们的方法是一种基于遗传算法的方法,其基于环形拓扑产生初始群体,并通过遗传算子在能量消耗方面产生更好的不规则拓扑。所提出的方法的目标函数是最小化网络通信产生的能量消耗。我们在几个多媒体基准和自定义生成图上测试了我们的方法,并将其与先前的算法和环形拓扑进行了比较。我们的结果表明,随着应用程序节点的数量增加,我们的方法在比以前的方法更短的时间内实现更好的结果。 (c)2018年elestvier b.v.保留所有权利。

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