The growing need for additional primary memory capacity in data centers and exascale computing is driving the integration of DRAM and NVMs, which utilizes the advantages of both memory types and lessens the drawbacks of these memory types. However, because of its limitations in terms of write endurance and write latency, NVM integration in hybrid memory might be challenging. To maximize main memory capacity and ensure efficient use of PCM, migrating write-intensive pages to DRAM enables better overall performance. Accurately identifying migration candidates is non-trivial: static threshold-based schemes either move pages at fixed intervals or, whenever write counts cross a preset cutoff, often misplace hot data. More sophisticated machine-intelligent methods (e.g., LSTM- or Attention-based predictors, Deep RL) can adapt to complex access patterns but incur prohibitive training and inference overheads for on-chip deployment.
In this paper, we present a lightweight, online RL-based migration scheme: PEARL. PEARL is a tabular Q-learning/bandit-style algorithm that maintains per-page "confidence" (Q-value), updates based on the access patterns, and drives promotion to DRAM or eviction to NVM. The proposed method is validated against state-of-the-art threshold-based migration techniques using SPEC 2017 benchmark suite applications. The method improves performance by 6% and reduces total energy consumption by 16%, demonstrating a practical path to higher performance and greater NVM endurance.