Enhanced Pruning Strategy for Multi-Component Neural Architectures Using Component-Aware Graph Analysis

Deep neural networks (DNNs) deliver outstanding performance, but their complexity often prohibits deployment in resource-constrained settings. Comprehensive structured pruning frameworks based on parameter dependency analysis reduce model size with specific regard to computational performance. When applying them to Multi-Component Neural Architectures (MCNAs), they risk network integrity by removing large parameter groups. We introduce a component-aware pruning strategy, extending dependency graphs to isolate individual components and inter-component flows. This creates smaller, targeted pruning groups that conserve functional integrity. Demonstrated effectively on a control task, our approach achieves greater sparsity and reduced performance degradation, opening a path for optimizing complex, multi-component DNNs efficiently.

@article{sundaram2025_2504.13296,
  title={ Enhanced Pruning Strategy for Multi-Component Neural Architectures Using Component-Aware Graph Analysis },
  author={ Ganesh Sundaram and Jonas Ulmen and Daniel Görges },
  journal={arXiv preprint arXiv:2504.13296},
  year={ 2025 }
}