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Incremental Run-time Application Mapping for Heterogeneous Network on Chip

机译:芯片上异构网络的增量运行时间应用映射

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Multiple heterogeneous processor systems on chip are more and more widely used in order to provide a higher system performance. Run-time mapping for heterogeneous Network on Chip (NoC) is challenging since the sequence of the incoming applications is unknown in advance. This paper presents Heterogeneous Near Convex Region Algorithm (HNCR), an incremental run-time mapping algorithm for heterogeneous NoC. Novel application description and energy model are introduced for heterogeneous NoC. We adjust the idea of near convex region to fit the need of heterogeneous mapping. Experimental results show that HNCR gains 16.7% and 60.5% average reductions in network latency compared to greedy and random solutions, together with 17.9% and 54.9% average reductions in communication energy only at the cost of a slight increase in total execution time. The experiments also show the extensibility of our scheme under different injecting rates and traffic distribution models.
机译:芯片上的多个异构处理器系统越来越广泛地使用,以便提供更高的系统性能。由于进入应用程序的顺序提前未知,因此芯片上的异构网络(NOC)的运行时映射是具有挑战性的。本文呈现了异构近凸区算法(HNCR),是异构NOC的增量运行时映射算法。引入了新的应用描述和能量模型的异质NOC。我们调整凸面区域附近的思想,以满足异构映射的需要。实验结果表明,与贪婪和随机解决方案相比,HNCR在网络延迟中的平均降低了16.7%和60.5%,同时只有17.9%和54.9%的平均减少通信能量,只需略微增加总执行时间。实验还显示了我们的计划在不同的注射率和交通分布模型下的可扩展性。

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