Integrating kernel normalization into other types of neural network architectures beyond CNNs is feasible and offers opportunities for improving model performance in various domains. For instance, incorporating kernel normalization into recurrent neural networks (RNNs) or transformer models could help capture long-range dependencies more effectively by considering spatial correlations among elements during normalization. By adapting the principles of KernelNorm and KNConv layers to suit the architecture-specific requirements of different neural network structures, researchers can explore novel ways to enhance model training stability, convergence speed, and generalization capabilities across diverse tasks.