CARN: Complexity-Aware Routing Network for Efficient and Adaptive Inference

Deep neural networks (DNNs) have achieved remarkable success across various domains, yet their rigid, static computation graphs lead to significant inefficiencies in real-world deployment. Standard architectures allocate equal computational resources to all inputs, disregarding their inherent complexity, which results in unnecessary computation for simple samples and suboptimal processing for complex ones. To address this, we propose the Complexity-Aware Routing Network (CARN), a novel framework that dynamically adjusts computational pathways based on input complexity. CARN integrates a self-supervised complexity estimation module that quantifies input difficulty using confidence, entropy, and computational cost, guiding a neural network-based routing mechanism to optimally assign task modules. The model is trained using a routing loss function that balances assignment accuracy and computational efficiency, mitigating expert starvation while preserving specialization. Extensive experiments on CIFAR-10, CIFAR-100, and Tiny-ImageNet demonstrate that CARN achieves up to 4x reduction in computational cost and over 10x reduction in parameter movement while maintaining high accuracy compared to state-of-the-art static models. The code and pre-trained models are made available at https://github.com/rrgaire/CARN for reproducibility and further research.