The increasing density of many-core architectures necessitates interconnection networks that are both high-performance and fault-resilient. Eisenstein-Jacobi (EJ) networks, with their symmetric 6-regular topology, offer superior topological properties but challenge traditional routing heuristics under fault conditions. This paper evaluates three routing paradigms in faulty EJ environments: deterministic Greedy Adaptive Routing, theoretically optimal Dijkstra's algorithm, and a reinforcement learning (RL)-based approach. Using a multi-objective reward function to penalize fault proximity and reward path efficiency, the RL agent learns to navigate around clustered failures that typically induce dead-ends in greedy geometric routing. Dijkstra's algorithm establishes the theoretical performance ceiling by computing globally optimal paths with complete topology knowledge, revealing the true connectivity limits of faulty networks. Quantitative analysis at nine faulty nodes shows greedy routing catastrophically degrades to 10% effective reachability and packet delivery, while Dijkstra proves 52-54% represents the topological optimum. The RL agent achieves 94% effective reachability and 91% packet delivery, making it suitable for distributed deployment. Furthermore, throughput evaluations demonstrate that RL sustains over 90% normalized throughput across all loads, actually outperforming Dijkstra under congestion through implicit load balancing strategies. These results establish RL-based adaptive policies as a practical solution that bridges the gap between greedy's efficiency and Dijkstra's optimality, providing robust, self-healing communication in fault-prone interconnection networks without requiring the global topology knowledge or computational overhead of optimal algorithms.

翻译：随着众核架构集成密度的不断提升，对兼具高性能与故障弹性的互连网络提出了迫切需求。Eisenstein-Jacobi（EJ）网络凭借其对称的六正则拓扑结构提供了优越的拓扑特性，但在故障条件下对传统路由启发式方法构成了挑战。本文评估了故障EJ环境中的三种路由范式：确定性贪婪自适应路由、理论最优的Dijkstra算法以及基于强化学习（RL）的方法。通过采用多目标奖励函数——对接近故障区域的行为进行惩罚同时对路径效率进行奖励，RL智能体学会了在通常导致贪婪几何路由陷入死锁的集群故障周围进行导航。Dijkstra算法凭借完整的拓扑知识计算全局最优路径，确立了理论性能上限，揭示了故障网络真实的连通性极限。在九个故障节点的定量分析中，贪婪路由的有效可达性与数据包投递率灾难性下降至10%，而Dijkstra算法证明52-54%代表了拓扑最优值。RL智能体实现了94%的有效可达性与91%的数据包投递率，适用于分布式部署。此外，吞吐量评估表明RL在所有负载下均能维持90%以上的归一化吞吐量，其隐式负载均衡策略在拥塞条件下实际表现优于Dijkstra算法。这些结果确立了基于RL的自适应策略作为一种实用解决方案，它弥合了贪婪路由的效率与Dijkstra算法的最优性之间的鸿沟，能够在无需全局拓扑知识或最优算法计算开销的情况下，为易故障互连网络提供鲁棒的自愈通信能力。