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ReLMXEL: Adaptive RL-Based Memory Controller with Explainable Energy and Latency Optimization
Panuganti Chirag Sai, Gandholi Sarat, R. Raghunatha Sarma, Venkata Kalyan Tavva, Naveen M
TL;DR
Memory systems need lower latency and energy use, but changing workloads challenge static or conventional machine-learning approaches and RL decisions often lack transparency. ReLMXEL uses explainable multi-agent online reinforcement learning with reward decomposition and detailed memory metrics to tune controller parameters. Across diverse workloads, it reports improved energy consumption, bandwidth utilization, and latency-related performance while exposing trade-offs among objectives.
Problem
Memory systems require improved latency and energy efficiency under dynamic workloads, while RL-driven controller decisions lack transparency needed for explainability, reliability, and trust.
Method
ReLMXEL is an explainable multi-agent online RL framework that tunes memory-controller parameters using detailed behavior metrics, reward decomposition, warmup exploration, and workload trace feedback.
Results
Across diverse workloads, ReLMXEL reports reduced energy consumption, high bandwidth utilization, and reduced latency or only slight latency increases depending on workload.
Takeaways & Limitations
The framework combines adaptive memory optimization with interpretable decisions to balance competing energy, bandwidth, and latency objectives.
Abstract
from arXiv · showhide
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.
I. INTRODUCTION
DRAM efficiency is central to system performance, motivating reinforcement-learning memory controllers that reduce latency and energy while adapting to diverse workloads. ReLMXEL addresses limited transparency in RL-driven decisions through an explainable multi-agent design.
- DRAM is critical to system performance, especially for memory- and compute-intensive workloads such as ML training and inference.
- Prior work uses reinforcement learning to design self-optimizing memory controllers that adapt scheduling decisions across workloads.
- RL-driven memory-controller decisions remain insufficiently transparent for systems requiring explainability, reliability, and trust.
- ReLMXEL dynamically tunes memory policies for latency and energy across dense, memory-bound, and irregular workloads while making decisions interpretable.
- The framework aims to balance performance with accountability in complex computing environments.
II. LITERATURE REVIEW
The literature frames reinforcement learning as suitable for dynamic memory systems because it adapts through interaction and optimizes cumulative, potentially multi-objective rewards. The section also introduces the standard agent–environment learning loop.
- An RL agent observes a state, selects an action under policy π(a|s), receives a reward, and transitions to a new state at each timestep.
- RL seeks a policy that maximizes expected cumulative reward over time through iterative interaction with the environment.
- Traditional machine-learning methods assume large labeled datasets and stationary distributions, whereas memory workloads can change rapidly.
- Direct environmental interaction lets RL adapt policies using real-time feedback in non-stationary memory systems.
- RL also supports multi-objective optimization, including balancing energy and efficiency-related objectives.
A. Self-Optimizing Memory Controllers: A Reinforcement Learning Approach
Ipek et al.’s self-optimizing memory controller models DRAM command scheduling as reinforcement learning rather than relying on static policies. Its agent observes memory-system features, selects commands, and updates action values with SARSA and CMAC.
- The approach dynamically adapts command scheduling to overcome limitations of static DRAM controllers.
- The controller models processor cores, caches, buses, DRAM banks, and scheduling queues as an RL environment.
- Its state includes read/write counts and load misses, while actions select Precharge, Activate, Read-CAS, Write-CAS, REF, or NOP commands.
- The agent receives reward 1 for read/write commands and 0 otherwise.
- SARSA updates Q-values using a CMAC function approximator for adaptive command scheduling.
B. Pythia: A Customizable Hardware Prefetching Framework Using Online Reinforcement Learning
Pythia applies online reinforcement learning to cache prefetching by mapping program context to prefetch-offset decisions. Its reward incorporates prefetch accuracy, timeliness, coverage, and system bandwidth feedback.
- Pythia treats the cache prefetcher as an RL agent that observes program context for each demand request.
- The reward incorporates current memory-bandwidth usage to make prefetch decisions system-aware.
- Its objective is to maximize accurate and timely prefetch requests through an optimized prefetching policy.
- The agent selects a prefetch offset from a predetermined set of offsets.
- Pythia’s reward evaluates Accurate and Timely, Accurate but Late, Loss of Coverage, Inaccurate, and No Prefetch outcomes.
C. Reinforcement Learning using Reward Decomposition
Reward decomposition replaces a scalar reinforcement-learning reward with component-wise rewards, enabling explanations of why one action is preferred over another. RDX compares component advantages, while MSX identifies a minimal sufficient subset supporting the preference.
- The scalar reward is decomposed into a reward vector whose elements represent rewards from specific components.
- Reward Difference Explanation compares two actions by showing each component’s difference in expected return.
- A positive component difference favors a1 over a2, whereas a negative difference favors a2.
- Minimal Sufficient Explanation selects the smallest subset of components whose cumulative advantage justifies preferring one action over another.
III. RELMXEL
ReLMXEL is a multi-agent reinforcement-learning memory controller that observes detailed DRAM behavior, decomposes rewards by performance metric, and dynamically tunes configurable controller parameters. Its algorithm uses exploration, per-agent Q-tables, and SARSA updates to optimize cumulative reward.
- The memory controller supplies latency, power, energy, bandwidth, switching, and row-buffer metrics as observations for reinforcement learning.Latency is tracked per request, while energy and transition metrics are derived from DRAM activity and state changes.
- ReLMXEL actions configure DRAM policies including page policy, request scheduler, scheduler buffer, and arbiter choices.
- Each configurable parameter has a Q-table, and agents select action vectors using an epsilon-greedy strategy during iterative controller interaction.
- The framework computes metric-wise rewards, decomposes Q-values by reward type, and updates them using the SARSA rule.
- A warmup threshold reserves initial iterations for exploration before optimization, with distinct seeds generated for individual agents.
A. Explainability of ReLMXEL
ReLMXEL explains action choices by decomposing system-level rewards and applying RDX and MSX to identify the components supporting a selected action. Examples show how energy, latency, and bandwidth trade-offs shape explanations.
- ReLMXEL decomposes the Q-function into individual Q-values for each system-level reward type before comparing actions.
- RDX and MSX provide increasingly concise rationales for selecting a1 over a2 by considering component advantages and disadvantages.
- For page policies, MSX can justify a closed-policy action when its energy improvement outweighs latency and bandwidth drawbacks.
- For arbitration policies, MSX can justify simple arbitration when its energy reduction is sufficient despite latency and bandwidth losses.
IV. EXPERIMENTAL SETUP AND RESULTS
Experiments evaluate ReLMXEL with DDR4 in DRAMSys across GEMM, STREAM, BFS, and selected SPEC CPU 2017 workloads. The setup combines a defined baseline controller with detailed latency, power, energy, bandwidth, switching, and row-buffer measurements.
- The evaluation uses DDR4 memory in DRAMSys with a single-channel, single-rank configuration and four bank groups containing four banks each.
- The baseline controller uses OpenAdaptive Page Policy and FR-FCFS scheduling, with OpenAdaptive reported as outperforming static open and closed policies.
- Workloads include GEMM, STREAM, BFS, and SPEC CPU 2017 traces covering dense linear algebra, vector operations, graph traversal, and compute-intensive applications.
- DRAMSys with DRAMPower measures latency, average power, total energy, and bandwidth, while additional metrics track bank switches, bank-group switches, row-buffer hits, and misses.
- SPEC CPU 2017 traces are generated with ChampSim by monitoring last-level-cache misses during simulations executing at least ten billion instructions.
A. Results
ReLMXEL’s feedback-driven tuning improves energy, bandwidth utilization, and latency across workload types, while exposing workload-specific trade-offs. The results emphasize balanced gains, with limited latency degradation in the reported aggregate comparison.
- Experimental setup: ReLMXEL uses trace splitting and tuned exploration to evaluate workload responses during parameter optimization.The trace split is set to 30,000, while epsilon_new is set to 0.001 because larger values hinder convergence and smaller values slow recovery.
- Workload-specific results: ReLMXEL improves bandwidth utilization and reduces latency while providing slightly better energy efficiency for memory-bound workloads such as STREAM and GEMM.The reported pattern specifically characterizes memory-bound workloads rather than all evaluated applications.
- Workload-specific results: ReLMXEL improves energy consumption and bandwidth utilization for mcf and omnetpp, but incurs a slight latency increase.This result indicates a trade-off between energy efficiency and data-transfer overhead in high-memory-traffic or communication-demand workloads.
- Adaptive optimization: ReLMXEL’s dynamic tuning incrementally adapts memory-controller parameters to real-time workload characteristics through feedback-driven optimization.Figures 3, 4, and 5 illustrate this step-by-step adaptation across energy, bandwidth, and latency measures.
- Overall outcome: Across diverse workloads, ReLMXEL achieves energy reductions and bandwidth gains without substantial latency degradation.The reported improvements are particularly associated with memory-bound and irregular workload patterns.
V. CONCLUSION AND FUTURE DIRECTIONS
ReLMXEL combines adaptive reinforcement learning with reward decomposition to optimize memory-controller parameters and balance competing objectives. The authors identify extensions toward heterogeneous memories and hardware-in-the-loop evaluation as future directions.
- Conclusion: ReLMXEL decomposes rewards into energy, bandwidth, and latency components to optimize memory-controller parameters while improving efficiency and transparency.The framework is reported to balance competing system objectives across diverse workloads.
- Future directions: Future work includes extending reinforcement-learning optimization to heterogeneous memory architectures and evaluating robustness in real-world scenarios.Hybrid nonvolatile memory systems are given as an example target architecture.
- Future directions: Hardware-in-the-loop integration is proposed to enable real-time interaction with actual hardware and bridge simulation-based evaluation with real-world applications.This direction is presented as an extension of the current adaptive memory-controller approach.