采用加权轮询仲裁的片上网络的理論分析与评估
Theoretical Analysis and Evaluation of NoCs with Weighted Round-Robin Arbitration
AI总结：
本文首次提出针对采用加权轮询仲裁的片上网络的性能分析技术，通过估计有效服务时间并结合分解方法扩展到任意规模NoC，在实际应用中误差低于5%。
摘要：
快速且准确的性能分析技术在早期设计空间探索和硅前评估（包括软件生态系统开发）中至关重要。特别是随着众核处理器规模不断扩大，片上通信持续发挥着日益重要的作用。本文提出了首个针对采用加权轮询（WRR）仲裁的片上网络（NoCs）的性能分析技术。除了公平性之外，WRR仲裁还提供了按流量类别重要性比例分配带宽的灵活性，这与基本的轮询仲裁和基于优先级的仲裁不同。所提出的方法首先估计由于WRR仲裁导致的队列中数据包的有效服务时间。然后，利用有效服务时间计算数据包的平均等待时间。接着，我们引入一种分解技术来扩展分析模型，以处理任意规模的NoC。所提出的方法在执行实际应用时误差小于5%，在具有不同突发性水平的挑战性合成流量下误差为10%。
英文摘要：
Fast and accurate performance analysis techniques are essential in early design space exploration and pre-silicon evaluations, including software eco-system development. In particular, on-chip communication continues to play an increasingly important role as the many-core processors scale up. This paper presents the first performance analysis technique that targets networks-on-chip (NoCs) that employ weighted round-robin (WRR) arbitration. Besides fairness, WRR arbitration provides flexibility in allocating bandwidth proportionally to the importance of the traffic classes, unlike basic round-robin and priority-based arbitration. The proposed approach first estimates the effective service time of the packets in the queue due to WRR arbitration. Then, it uses the effective service time to compute the average waiting time of the packets. Next, we incorporate a decomposition technique to extend the analytical model to handle NoC of any size. The proposed approach achieves less than 5% error while executing real applications and 10% error under challenging synthetic traffic with different burstiness levels.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
