Drift-Aware Regularization for Long-Term Stability in Phase-Change Memory Based Neural Network Implementations

Phase-Change Memory (PCM) based resistive crossbars are a promising technology for energy-efficient neural network inference in analog in-memory computing (AIMC) systems. However, a conductance drift in PCM devices degrades performance over time, posing a challenge for applications requiring long-term stability. In order to overcome such problems, we propose a drift-aware regularization framework that stabilizes weight drift during training, complemented by an attention-based mechanism to prioritize critical features. Our approach significantly improves upon traditional hardware-aware (HWA) training and achieves more robust inference performance in the presence of conductance drift. In a five-year drift simulation, our method reduces classification error to 3.75%, compared to 9.14% with standard HWA training on a benchmark two-layer perceptron for the MNIST dataset. When combined with global drift compensation, the error is further reduced to 2.12%. These results demonstrate the effectiveness of our drift-aware regularization in enhancing the stability and accuracy of neural networks on AIMC hardware, offering a scalable, energy-efficient solution for inference in resource-constrained environments.