Reducing latency and energy consumption is critical to improving the efficiency of memory systems in modern computing. This work introduces ReLMXEL (Reinforcement Learning for Memory Controller with Explainable Energy and Latency Optimization), a explainable multi-agent online reinforcement learning framework that dynamically optimizes memory controller parameters using reward decomposition. ReLMXEL operates within the memory controller, leveraging detailed memory behavior metrics to guide decision-making. Experimental evaluations across diverse workloads demonstrate consistent performance gains over baseline configurations, with refinements driven by workload-specific memory access behaviour. By incorporating explainability into the learning process, ReLMXEL not only enhances performance but also increases the transparency of control decisions, paving the way for more accountable and adaptive memory system designs.

翻译：降低延迟与能耗对于提升现代计算中内存系统的效率至关重要。本文提出ReLMXEL（基于强化学习的可解释性能量与延迟优化内存控制器），这是一种可解释的多智能体在线强化学习框架，通过奖励分解动态优化内存控制器参数。ReLMXEL在内存控制器内部运行，利用详细的内存行为指标指导决策。在不同工作负载下的实验评估表明，相较于基线配置，该框架能持续获得性能提升，其优化由工作负载特定的内存访问行为驱动。通过将可解释性融入学习过程，ReLMXEL不仅提升了性能，还增强了控制决策的透明度，为构建更具可问责性和自适应性的内存系统设计铺平了道路。