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

arXiv: https://arxiv.org/abs/2504.13296 · HTML: https://mlsanity.com/paper/2504.13296/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 28 references that are in the map.
- First posted on arXiv 17 Apr 2025.

## Builds on (most cited first, 17 of 17)

- [The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks](https://mlsanity.com/paper/1803.03635/) (2018)
- [Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture](https://mlsanity.com/paper/2301.08243/) (2023)
- [Temporal Difference Learning for Model Predictive Control](https://mlsanity.com/paper/2203.04955/) (2022)
- [DepGraph: Towards Any Structural Pruning](https://mlsanity.com/paper/2301.12900/) (2023)
- [A Survey on Deep Neural Network Pruning-Taxonomy, Comparison, Analysis, and Recommendations](https://mlsanity.com/paper/2308.06767/) (2023)
- [Structured Pruning for Deep Convolutional Neural Networks: A survey](https://mlsanity.com/paper/2303.00566/) (2023)
- [Differentiable Joint Pruning and Quantization for Hardware Efficiency](https://mlsanity.com/paper/2007.10463/) (2020)
- [Structural Pruning via Latency-Saliency Knapsack](https://mlsanity.com/paper/2210.06659/) (2022)
- [An Operator Theoretic View on Pruning Deep Neural Networks](https://mlsanity.com/paper/2110.14856/) (2021)
- [Is Complexity Required for Neural Network Pruning? A Case Study on Global Magnitude Pruning](https://mlsanity.com/paper/2209.14624/) (2022)
- [Class-Aware Pruning for Efficient Neural Networks](https://mlsanity.com/paper/2312.05875/) (2023)
- [Structurally Prune Anything: Any Architecture, Any Framework, Any Time](https://mlsanity.com/paper/2403.18955/) (2024)
- [Automatic Mapping of the Best-Suited DNN Pruning Schemes for Real-Time Mobile Acceleration](https://mlsanity.com/paper/2111.11581/) (2021)
- [Boosting Pruned Networks with Linear Over-parameterization](https://mlsanity.com/paper/2204.11444/) (2022)
- [Iterative Filter Pruning for Concatenation-based CNN Architectures](https://mlsanity.com/paper/2405.03715/) (2024)
- [Resource Efficient Neural Networks Using Hessian Based Pruning](https://mlsanity.com/paper/2306.07030/) (2023)
- [Decay Pruning Method: Smooth Pruning With a Self-Rectifying Procedure](https://mlsanity.com/paper/2406.03879/) (2024)

## Built on by (most cited first, 3 of 3)

- [Application-Specific Component-Aware Structured Pruning of Deep Neural Networks in Control via Soft Coefficient Optimization](https://mlsanity.com/paper/2507.14882/) (2025)
- [COMponent-Aware Pruning for Accelerated Control Tasks in Latent Space Models](https://mlsanity.com/paper/2508.08144/) (2025)
- [Component-Aware Pruning Framework for Neural Network Controllers via Gradient-Based Importance Estimation](https://mlsanity.com/paper/2601.19794/) (2026)

## Abstract (arXiv)

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.