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Bi-LCQ: A low-weight clustering-based Q-learning approach for NoCs

机译：Bi-LCQ：针对NoC的基于轻量级聚类的Q学习方法

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摘要

Network congestion has a negative impact on the performance of on-chip networks due to the increased packet latency. Many congestion-aware routing algorithms have been developed to alleviate traffic congestion over the network. In this paper, we propose a congestion-aware routing algorithm based on the Q-learning approach for avoiding congested areas in the network. By using the learning method, local and global congestion information of the network is provided for each switch. This information can be dynamically updated, when a switch receives a packet. However, Q-learning approach suffers from high area overhead in NoCs due to the need for a large routing table in each switch. In order to reduce the area overhead, we also present a clustering approach that decreases the number of routing tables by the factor of 4. Results show that the proposed approach achieves a significant performance improvement over the traditional Q-learning, C-routing, DBAR and Dynamic XY algorithms.
机译：由于增加的数据包延迟，网络拥塞会对片上网络的性能产生负面影响。已经开发了许多拥塞感知路由算法来减轻网络上的流量拥塞。在本文中，我们提出了一种基于Q学习方法的拥塞感知路由算法，可避免网络中的拥塞区域。通过使用学习方法，为每个交换机提供了网络的本地和全局拥塞信息。当交换机收到数据包时，可以动态更新此信息。然而，由于每个交换机中都需要一个较大的路由表，因此，Q学习方法在NoC中存在较大的区域开销。为了减少区域开销，我们还提出了一种集群方法，该方法将路由表的数量减少了4倍。结果表明，与传统的Q学习，C路由，DBAR相比，该方法可以显着提高性能。和动态XY算法。

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