As the number of datasets processed in computing systems has increased in recent years, there is growing demand for high capacity main memory subsystems. However, further increases in the capacity of conventional DRAM-based main memory systems have stalled due to scaling limitations. Recent studies have shown that PCM, which can provide greater capacity than DRAM, is emerging as a candidate for high capacity memory. However, PCM suffers from problems related to the thermal mechanisms employed for storing data. The Write Disturbance (WD) phenomenon occurs when the thermal mechanisms of the PCM severely damage the data reliability of proximate cells. WD in PCM has become more significant below 20 nm. In this paper, we propose Sparse-Insertion Write Cache (SIWC), a practical, low-cost approach to mitigate WD errors. In PCM, repeated writes gradually degrade the validity of data in neighboring cells. SIWC uses a private write cache for the PCM write data to prevent repeated writes to the same address. The sparse-insertion technique can reduce cache eviction and minimize increases in the total write count. Our experimental results show that SIWC effectively reduces repeated writes and reduces the number of WD-vulnerable addresses across a wide range of applications.

Current computer systems require large memory capacities to manage the tremendous volume of datasets. A DRAM cell consists of a transistor and a capacitor, and their size has a direct impact on DRAM density. While technology scaling can provide higher density, this benefit comes at the expense of low drivability, due to the increase in series resistance of the smaller transistor, which slows the process of restoring the charge in cells. DRAM operations require recovery processes due to the destructive nature of DRAM cells. Among such operations, the write recovery process has the most difficulty in meeting the timing constraints. In this paper, we explore an intrinsic mechanism in the DRAM write operation, and find a relation between restoration and retention times. Based on our observation, we propose a practical mechanism, Relaxed Refresh with Compensated Write Recovery (RRCW), which efficiently mitigates refresh overheads by providing longer restoration periods. Furthermore, to minimize the penalty of the longer restoration, we also introduce another mechanism, Refresh-Aware Write Recovery (RAWR), which appropriately curtails longer recovery time according to the waiting time until being refreshed. Lastly, we introduce a scheduling policy to efficiently utilize RAWR. Evaluations show that the benefits of our mechanisms increase as memory intensity increases.