RRAM-based in-memory computing (IMC) offers high energy efficiency but suffers from conductance drift that severely degrades long-term accuracy. Existing approaches including retraining, noise-aware training, and Batch Normalization (BN)-based calibration either require RRAM rewriting, demand large storage overhead, or rely on online correction. We propose VeRA+, a lightweight drift compensation framework that reuses shared projection matrices and introduces only two compact drift-specific vectors per drift level. A drift-aware scheduling algorithm offline-trains a small set of VeRA+ parameters and selects the appropriate set over time without any on-chip retraining or data replay. VeRA+ preserves up to 99.77% of the drift-free accuracy after ten years of simulated drift and reduces storage overhead by more than three orders of magnitude compared with BN-based calibration. To validate VeRA+ under realistic device behavior, we extract one-week drift statistics from measurements on our fabricated 1T1R RRAM devices and use them to simulate realistic drifted weights. Under these measured drift conditions, VeRA+ achieves accuracy close to the drift-free baseline, providing an efficient and practical solution for long-term drift resilience in RRAM-IMC.

翻译：基于RRAM的存内计算（IMC）能效高，但面临电导漂移问题，这会严重降低长期精度。现有方法包括再训练、噪声感知训练和基于批归一化（BN）的校准，它们或需要RRAM重写，或要求大存储开销，或依赖在线校正。我们提出VeRA+，一种轻量级漂移补偿框架，该框架复用共享投影矩阵，并仅对每个漂移等级引入两个紧凑的漂移专用向量。一种漂移感知调度算法离线训练一小部分VeRA+参数，并随时间推移选择合适参数集，无需任何片上再训练或数据回放。在模拟十年漂移后，VeRA+可保留高达99.77%的无漂移精度，且与基于BN的校准相比，存储开销降低三个以上数量级。为在真实器件行为下验证VeRA+，我们从自研1T1R RRAM器件的实测结果中提取一周漂移统计数据，并将其用于模拟真实漂移权值。在这些实测漂移条件下，VeRA+的精度接近无漂移基线，为RRAM-IMC的长期抗漂移提供了一种高效且实用的解决方案。