RLRM: Reinforcement Learning-Based Routing for Ring-Augmented Mesh NoC

Abstract

Modern multicore processors increasingly rely on network-on-chip (NoC) architectures to support high-bandwidth and low-latency communication. Traditional mesh-based NoC topologies suffer from uneven load distribution, with central router nodes frequently becoming congestion hot spots. The existing approaches mainly focus on introducing bypass links or independent ring interconnects. However, bypass links can only alleviate congestion between fixed routers, and routers not included in the ring still rely on conventional mesh forwarding, limiting the overall performance improvement. To address these challenges, this article proposes a reinforcement learning-based routing for ring-augmented mesh NoC (RLRM) to improve communication efficiency in multicore processors. First, we design a ring-augmented $8\times 8$ mesh topology that integrates horizontal and vertical ring interconnects to achieve balanced load distribution and mitigate congestion hotspots. Second, we equip each router with a topology-aware reinforcement learning (RL) agent that dynamically selects paths based on congestion conditions, thereby proactively avoiding performance bottlenecks. Extensive experiments demonstrate that the proposed solution provides a scalable and efficient architecture for high-performance computing and multicore NoC systems. Experimental results under realistic benchmark traffic patterns show that RLRM reduces average latency by 22.78% compared to conventional mesh and static hybrid routing schemes.