RWriC: A Dynamic Writing Scheme for Variation Compensation for RRAM-based In-Memory Computing

RRAM-based compute-in-memory (CIM) suffers from programming variation issues, specifically device-to-device variation (DDV) and cycle-to-cycle variation (CCV), which can have a detrimental impact on inference accuracy. To address these variation issues, we propose RWriC, a dynamic Writing scheme for variation Compensation for RRAM-based CIM. RWriC sequentially programs the weights, implemented by multiple RRAM cells, starting from the high significance cell (HSC) and moving towards the low significance cell (LSC). This approach leverages the knowledge of current cumulative errors and the programming targets (PTs) of other RRAM cells to dynamically adjust the PT of the RRAM currently under programming. By shifting the PT of HSC, RWriC enables the LSC to compensate for the programming errors of the HSC. Moreover, when the variation is substantial, RWriC allows the magnitude of LSC to be scaled up, providing an even wider compensation range. Through the combined application of the shifting and scaling techniques, experimental results show that the inference accuracy for ResNet50 on the CIFAR-10 dataset only drops by 0.9% under 18% device variation. In comparison to the conventional writing scheme, our RWriC approach achieves a 5-11x improvement in variation robustness for ResNet50 and Yolov8 across different tasks.