ReLMXEL：基于自适应强化学习的可解释性能耗与延迟优化内存控制器

摘要

降低延迟与能耗对提升现代计算中内存系统效率至关重要。本文提出ReLMXEL（基于可解释能耗与延迟优化的内存控制器强化学习框架），这一可解释多智能体在线强化学习框架通过奖励分解机制动态优化内存控制器参数。ReLMXEL在内存控制器内部运行，利用细粒度的内存行为指标指导决策。多样化工作负载下的实验评估表明，该框架在基准配置基础上实现了持续性能提升，且优化效果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不仅提升了系统性能，更增强了控制决策的透明度，为构建更具可问责性与自适应能力的内存系统设计开辟了新途径。

English

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.

ReLMXEL：基于自适应强化学习的可解释性能耗与延迟优化内存控制器

ReLMXEL: Adaptive RL-Based Memory Controller with Explainable Energy and Latency Optimization

摘要

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