Reinforcement Learning Enabled Routing for High-Performance Networks-on-Chip
Md Farhadur Reza, Tung Le
Abstract
Network-on-chip (NoC) has been the standard fabric for multi-core architectures. With the increase in cores in the multi-core architectures, the probability of congestion increases because of longer path among sources and destinations in the NoC and because of the presence of multiple applications in a chip. Congestion hampers system performance because of the delay in packet delivery, which in turn results in reduced effective utilization of resources and reduced throughout. Higher congestion also results in higher energy consumption in NoC as packets spend more time in the network. Reactive detection and/or a single fixed routing algorithm are not effective to prevent congestion from happening for different traffic patterns in NoC. Therefore, we propose reinforcement learning based proactive routing technique that selects the best routing algorithm from multiple available routing algorithms using NoC utilization and congestion information to improve communication performance. Simulation results demonstrate latency performance improvement while providing robust NoC performance for different NoC states and traffic demands.