A Lightweight Congestion Control Technique for NoCs with Deflection Routing
Shruti Yadav Narayana, Sumit K. Mandal, Raid Zuhair Ayoub, Michael Kishinevsky, Umit Yusuf Ogras · 2023
Network-on-Chip (NoC) congestion builds up during heavy traffic load and leads to wasted link bandwidth, crippling the system performance. We propose a lightweight machine learning-based technique that helps predict congestion in the net-work by collecting features related to traffic at each destination and labelling it using a novel time reversal approach. The labelled 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$\boldsymbol{6\times 6}$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.