2026全国大学生计算机系统能力大赛
Projects with this topic
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Non-Volatile Memory (NVM) offers high density, low leakage power, and near-DRAM latency, but remains vulnerable to write disturbance, especially during RESET operations where thermal diffusion may disturb adjacent cells. Existing mitigation methods rely on error correction, coding, or fixed heuristics, and therefore adapt poorly to changing workloads and data patterns. We propose RLWD, a reinforcement-learning-based out-of-place update scheme that mitigates write disturbance in NVM. RLWD formulates write-address selection as a reinforcement learning problem, where the agent selects a target location that minimizes write disturbance based on the current write pattern and stale-block state. Through lightweight training, RLWD adapts online to workload changes and achieves long-term write-disturbance mitigation. Experiments show that RLWD reduces write disturbance by 18%--28%, write amplification by 10%~18%, and overall write cost by 8%~15% on average, with more than 30% disturbance reduction in some workloads. These results demonstrate that RLWD provides effective, low-overhead write-disturbance mitigation for NVM.
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东北大学秦皇岛分校,T2026191459910913,汤河施工队,proj57
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