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{arxiv.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}
}