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Overview of high-efficiency ant colony optimization (ACO)-based adaptive routings for traffic balancing in network-on-chip systems

机译:基于高效蚁群优化(ACO)的自适应路由概述,用于片上网络系统中的流量平衡

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摘要

The regular topology of mesh-based Network-on-Chip (NoC) provides flexible and scalable architecture for chip multiprocessor (CMP) systems. However, as the complexity of the network increases, the traffic congestion problems become major performance bottlenecks. An effective adaptive routing algorithm can help minimize path congestion through load balancing. However, conventional adaptive routing schemes only use current channel-based information to detect the congestion status. This information has difficulty showing the real congestion status under time-variant traffic patterns. To predict temporal network congestion, Ant Colony Optimization (ACO) based routing was proposed to identify the near-future non-congested path to a desired target according to historical network information. To design a high-efficiency ACO-based routing for traffic balancing in resource-limited NoCs, two major design issues should be considered, including 1) the selection efficiency and 2) the cost efficiency. In this paper, the design challenges and concepts of ACO-based adaptive routings are introduced. Besides, the representative related works are reviewed and summarized. Finally, we conclude the paper and point out the future works of ACO-based routing algorithms.
机译:基于网格的常规片上网络(NoC)的常规拓扑为芯片多处理器(CMP)系统提供了灵活且可扩展的体系结构。但是,随着网络复杂性的增加,流量拥塞问题成为主要的性能瓶颈。有效的自适应路由算法可以通过负载平衡帮助最大程度地减少路径拥塞。但是,常规的自适应路由方案仅使用基于当前信道的信息来检测拥塞状态。该信息难以显示时变流量模式下的实际拥塞状态。为了预测时间网络的拥塞,提出了一种基于蚁群优化(ACO)的路由,以根据历史网络信息来识别到达所需目标的近期未拥塞路径。为了设计一种基于ACO的高效路由,以在资源受限的NoC中实现流量平衡,应考虑两个主要设计问题,包括1)选择效率和2)成本效率。在本文中,介绍了基于ACO的自适应路由的设计挑战和概念。此外,对代表性的相关作品进行了回顾和总结。最后,我们总结了论文并指出了基于ACO的路由算法的未来工作。

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