Intelligent congestion control for NoC architecture in Gem5 simulator

Smriti Srivastava, M. Shaikh, G. Shivaneetha, Minal Moharir
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引用次数: 1

Abstract

Congestion in a network significantly impacts the performance of an NoC as there is a substantial increase in latency and power consumption. Machine Learning techniques aid in designing routing methods to keep the network cognizant of the traffic status. This paper presents a congestion-aware Q-routing algorithm based on the Q-learning model of reinforcement learning. The proposed algorithm enhances the network's performance in an NoC under heavy traffic conditions by routing the packets along a less congested path. Thus, it reduces the congestion in the network. This is possible as Q-learning allows the network to keep track of the local and non-local congestion by estimating Q-values. The Q-values guide a node in sending a data packet along an optimal path, thereby evading busy routes. The simulation done on the gem5 simulator with uniform link latency in the network exhibits that Q-routing performs better in a high-load environment than traditional XY and Odd-Even Routing methods, with a performance gain of 5.73% and 12.73%, respectively. The results for varied link latencies that were randomly assigned to create a practical congestion-probable scenario showed that the proposed method outperformed both the XY and Odd-Even routing algorithm with a respective performance gain of 7.38% and 15.19%.
Gem5仿真器中NoC架构的智能拥塞控制
网络中的拥塞会显著影响NoC的性能,因为这会大大增加延迟和功耗。机器学习技术有助于设计路由方法,以保持网络对流量状态的认知。提出了一种基于强化学习中的q -学习模型的感知拥塞q -路由算法。该算法通过将数据包沿较少拥塞的路径路由,提高了网络在大流量条件下的性能。因此,它减少了网络中的拥塞。这是可能的,因为q学习允许网络通过估计q值来跟踪本地和非本地拥塞。q值引导节点沿着最优路径发送数据包,从而避开繁忙的路由。在网络中均匀链路延迟的gem5模拟器上进行的仿真表明,Q-routing在高负载环境下的性能优于传统的XY和奇偶路由方法,性能增益分别为5.73%和12.73%。随机分配不同的链路延迟以创建实际的拥塞可能场景的结果表明,所提出的方法优于XY和奇偶路由算法,其性能增益分别为7.38%和15.19%。
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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