基于机器学习的低开销工业NoCs拥塞控制算法
Machine Learning-based Low Overhead Congestion Control Algorithm for Industrial NoCs
摘要：
片上网络（NoC）拥塞在高流量负载期间积累，并通过使核停滞而削弱系统性能。此外，拥塞导致由于阻塞的缓冲区和反弹的数据包而浪费的链路带宽。现有的方法在检测到拥塞后对核心进行节流，从而降低效率并不必要地浪费线路带宽。相比之下，我们提出了一种基于轻量级机器学习的技术，可以帮助预测网络中的拥塞。具体来说，我们提出的技术收集与每个目的地的流量相关的功能。然后，它使用一种新的时间反转方法标记的功能。标记的数据被用来设计一个低开销和可解释的决策树模型，用于运行时拥塞控制。在工业6 $\times$ 6 NoC上进行的仿真和真实流量实验表明，与现有的拥塞控制技术相比，所提方法的公平性和内存读取带宽提高了114%，而开销小于0.01%.
英文摘要：
Network-on-Chip (NoC) congestion builds up during heavy traffic load and cripples the system performance by stalling the cores. Moreover, congestion leads to wasted link bandwidth due to blocked buffers and bouncing packets. Existing approaches throttle the cores after congestion is detected, reducing efficiency and wasting line bandwidth unnecessarily. In contrast, we propose a lightweight machine learning-based technique that helps predict congestion in the network. Specifically, our proposed technique collects the features related to traffic at each destination. Then, it labels the features using a novel time reversal approach. The labeled data is used to design a low overhead and an explainable decision tree model used at runtime congestion control. Experimental evaluations with synthetic and real traffic on industrial 6$\times$6 NoC show that the proposed approach increases fairness and memory read bandwidth by up to 114\% with respect to existing congestion control technique while incurring less than 0.01\% of overhead.
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