Weight Transformations in Bit-Sliced Crossbar Arrays for Fault Tolerant Computing-in-Memory: Design Techniques and Evaluation Framework

The deployment of deep neural networks (DNNs) on compute-in-memory (CiM) accelerators offers significant energy savings and speed-up by reducing data movement during inference. However, the reliability of CiM-based systems is challenged by stuck-at faults (SAFs) in memory cells, which corrupt stored weights and lead to accuracy degradation. While closest value mapping (CVM) has been shown to partially mitigate these effects for multibit DNNs deployed on bit-sliced crossbars, its fault tolerance is often insufficient under high SAF rates or for complex tasks. In this work, we propose two training-free weight transformation techniques, sign-flip and bit-flip, that enhance SAF tolerance in multi-bit DNNs deployed on bit-sliced crossbar arrays. Sign-flip operates at the weight-column level by selecting between a weight and its negation, whereas bit-flip provides finer granularity by selectively inverting individual bit slices. Both methods expand the search space for fault-aware mappings, operate synergistically with CVM, and require no retraining or additional memory. To enable scalability, we introduce a look-up-table (LUT)-based framework that accelerates the computation of optimal transformations and supports rapid evaluation across models and fault rates. Extensive experiments on ResNet-18, ResNet-50, and ViT models with CIFAR-100 and ImageNet demonstrate that the proposed techniques recover most of the accuracy lost under SAF injection. Hardware analysis shows that these methods incur negligible overhead, with sign-flip leading to negligible energy, latency, and area cost, and bit-flip providing higher fault resilience with modest overheads. These results establish sign-flip and bit-flip as practical and scalable SAF-mitigation strategies for CiM-based DNN accelerators.