偏转路由延迟的解析模型:无缓冲noc的随机过程方法

K. Tatas
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引用次数: 3

摘要

无缓冲片上网络的性能分析和设计空间探索主要通过周期精确仿真来完成,耗时长,而分析模型目前似乎遥不可及。为了提高抽象水平并捕捉偏转路由固有的概率行为,本文提出了一种利用马尔可夫链模型分析无缓冲片上网络行为的方法。提出了一种将无缓冲NoC拓扑描述为离散时间马尔可夫链集的形式化方法。结果表明,将此描述与网络平均距离相结合,可以得到网络中任意对节点之间的跳数期望作为飞偏概率的函数。模型与周期精度仿真结果的对比表明,该方法在有效注入速率范围内具有较好的精度,计算成本可以忽略不计。
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Towards an Analytical Model of Latency in Deflection Routing: A Stochastic Process Approach for Bufferless NoCs
Performance analysis and design space exploration of bufferless Networks-on-Chip is done mainly through cycle accurate simulation which is time-consuming, while an analytical model seems out of reach for now. In order to raise the level of abstraction as well as capture the inherently probabilistic behavior of deflection routing, this paper presents a methodology for employing Markov chain models in the analysis of the behavior of bufferless Networks-on-Chip. A formal way of describing a bufferless NoC topology as a set of discrete-time Markov chains is presented. It is demonstrated that by combining this description with the network average distance, it is possible to obtain the expectation of the number of hops between any pair of nodes in the network as a function of the flit deflection probability. Comparisons between the proposed model and cycle-accurate simulation show that the proposed methodology achieves good accuracy at the useful injection rate range, with negligible computational cost.
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