RELAR: A Reinforcement Learning Framework for Adaptive Routing in Network-on-Chips

Adaptive routing is crucial to the overall performance of network-on-chips (NoCs), and still faces great challenges, especially when emerging applications on many-core architecture exhibit complicated and time-varying traffic patterns. When witnessing most existing heuristic adaptive routing algorithms fail to address multi-objective optimization for complex traffic well, we make the first attempt to propose a novel and comprehensive reinforcement learning framework for adaptive routing on NoCs, called RELAR. RELAR is suitable for diversified traffic patterns and resolve multi-objective optimization simultaneously. We conduct experiments against state-of-the-art routing algorithms to evaluate our design. The results show that RELAR achieves 14.82% reduction in packet latency on average, and reduces packet latency by up to 34.24% under heavy synthetic traffic workload.