Enhanced Pruning Strategy for Multi-Component Neural Architectures Using Component-Aware Graph Analysis
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866911065664651264 |
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| author | Sundaram, Ganesh Ulmen, Jonas Görges, Daniel |
| author_facet | Sundaram, Ganesh Ulmen, Jonas Görges, Daniel |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_13296 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Enhanced Pruning Strategy for Multi-Component Neural Architectures Using Component-Aware Graph Analysis Sundaram, Ganesh Ulmen, Jonas Görges, Daniel Machine Learning Artificial Intelligence 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. |
| title | Enhanced Pruning Strategy for Multi-Component Neural Architectures Using Component-Aware Graph Analysis |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2504.13296 |