ReLMXEL: Adaptive RL-Based Memory Controller with Explainable Energy and Latency Optimization
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.
69bc085c081b9ad49af56fc5
Panuganti Chirag Sai
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2026-03-23T10:55:58.849Z
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Panuganti Chirag Sai
Chirag9132
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Chirag9132
69bc085c081b9ad49af56fc6
Gandholi Sarat
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2026-03-24T09:23:06.510Z
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Gandholi Sarat
GandholiSarat
user
GandholiSarat
69bc085c081b9ad49af56fc7
R. Raghunatha Sarma
69bc085c081b9ad49af56fc8
Venkata Kalyan Tavva
69bc085c081b9ad49af56fc9
Naveen M
ReLMXEL: Adaptive RL-Based Memory Controller with Explainable Energy and Latency Optimization
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.