CoG-Guided Weight Correction for Fault-Tolerant Deep Neural Networks

The paper addresses the vulnerability of DNN weights to hardware faults in safety-critical systems. It introduces a Center of Gravity (CoG)-guided method that corrects faulty weights using spatial, distance-aware rules without retraining or architecture changes. Fault injection experiments on LSTM networks (StageNet, MTFNet) show up to 230x and 6.41x fault tolerance improvements at a BER of 10^{-3}, and on CNNs (ResNet-18, VGG-16) up to 49.55x and 20.79x improvements, with negligible accuracy loss. This matters because it is the first application of CoG to weight tensors, offering a practical, retraining-free approach to enhance DNN reliability in safety-critical applications.