Evaluation of genetic algorithm in network-on-chip based architecture

Doraisamy Radha, Minal Moharir
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Abstract

An increase in the number of cores gives a significant bounce in performance than an improvement in any of the factors or hardware. Many core systems use network-on-chip (NoC) for efficient communications among the cores in the system. However, the problem with NoC-based communication is that it significantly consumes a large amount of power and energy because the number of routers increases with the increase in the number of cores in the system. Power consumed by such components leads to degradation of the performance. The placement of cores in the topology is non-deterministic polynomial-time hardness (NP-Hard) problem. The optimal placement of cores in NoC is essential as it minimizes latency and communication costs. Thus, the NP-Hard problem of placing cores is solved using genetic algorithm (GA) based quadtree topology. The proposed work shows the analysis of GA-based quadtree topology, which outperforms other topologies in most aspects. The performance evaluation of GA-based quadtree topology is based on latency, throughput, power, area, bisection bandwidth, and diameter. Comparing these parameters with other topologies shows the prominence of the quadtree topology. The evaluation is performed in the Booksim simulator, and the experimental results revealed that the proposed GA-based quad tree-based topology is efficient for NoC-based communications.
评估基于芯片网络架构的遗传算法
内核数量的增加比任何因素或硬件的改进都能显著提升性能。许多内核系统使用片上网络(NoC)实现系统内核间的高效通信。然而,基于 NoC 的通信存在的问题是,由于路由器的数量会随着系统内核数量的增加而增加,因此会大量消耗电力和能源。这些组件消耗的功率会导致性能下降。在拓扑结构中放置内核是一个非确定性多项式时间困难(NP-Hard)问题。内核在 NoC 中的最佳位置至关重要,因为它能最大限度地减少延迟和通信成本。因此,基于四叉树拓扑结构的遗传算法(GA)解决了放置内核的 NP-Hard 问题。本论文展示了对基于 GA 的四叉树拓扑结构的分析,该拓扑结构在大多数方面都优于其他拓扑结构。基于 GA 的四叉树拓扑的性能评估基于延迟、吞吐量、功耗、面积、分段带宽和直径。将这些参数与其他拓扑结构进行比较,可以看出四叉树拓扑结构的优势。评估在 Booksim 仿真器中进行,实验结果表明,所提出的基于 GA 的四叉树拓扑结构在基于 NoC 的通信中是高效的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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