NoC容错的生物发芽算法实现

M. A. J. Sethi, F. Hussin, N. H. Hamid
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引用次数: 8

摘要

科学家们一直被生物技术所吸引,以解决工程世界的难题。这些技术正在成为解决片上网络(NoC)故障的新途径。为了满足处理元件(PE)的通信需求,设备的尺寸不断缩小,互连线的尺寸过大,导致了NoC中的故障。由于这些错误,人们提出了许多传统的容错技术。但是所有这些技术都有延迟、带宽利用率和吞吐量较低的缺点。本文提出了一种新的仿生技术“发芽”。仿生发芽算法基于生物脑技术,具有鲁棒性和NoC容错性。结果表明,该算法有效地利用了带宽和吞吐量,在网络故障恢复过程中,数据包网络延迟得到了很好的降低。在故障恢复期间,平均数据包网络延迟增加20.51%,NoC带宽减少0.471%,吞吐量下降至37.22%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Implementation of biological sprouting algorithm for NoC fault tolerance
Scientists are always attracted by the bio-inspired techniques to solve the difficult engineering world problems. These techniques are being used as the novel way to solve the faulty situation in Network on Chip (NoC). Faults in NoC arises due to big sizes of interconnects as the size of the devices were continuously reduced to cope with the communication requirement of processing elements (PE's). Due to these faults a lot of conventional fault tolerant techniques have been proposed. But all of these techniques have drawbacks of latency, less bandwidth utilization and lesser throughput. In this paper, a novel bio-inspired technique “sprouting” is proposed. Bio-inspired sprouting algorithm is based on biological brain technique which makes the algorithm robust and the NoC fault tolerant. The result shows that the bio-inspired algorithm efficiently utilizes the bandwidth and throughput, packet network latency is degrading gracefully during the network recovery from fault. The average packet network latency increases 20.51%, NoC bandwidth reduces 0.471% and throughput is drop to 37.22% during the recovery from faults.
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