Application-Specific Component-Aware Structured Pruning of Deep Neural Networks via Soft Coefficient Optimization

2025-07-20

AI Summary

To address the challenge of simultaneously achieving high compression ratios and maintaining application-specific performance in structured pruning of deep neural networks, this paper proposes a component-aware structured pruning method. Our approach introduces three key contributions: (1) an application-aware importance scoring framework that explicitly models the sensitivity of task-critical performance metrics—such as reconstruction fidelity—to individual network components; (2) a soft coefficient optimization mechanism that jointly optimizes pruning masks and weights, enabling continuous trade-offs between model sparsity and functional performance; and (3) a group-adaptive pruning intensity strategy that dynamically allocates sparsity budgets at the component level. Experiments on an MNIST autoencoder demonstrate that our method achieves over 70% parameter reduction while strictly satisfying application-specific constraints (e.g., reconstruction fidelity), significantly outperforming conventional structured pruning baselines.

Technology Category

• Machine Learning: Structured Learning
• Computer Vision: Learning & Optimization for CV
• Search and Optimization: Non-convex Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Web Mining and Content Analysis: Bridging structured and unstructured data

Abstract

Deep neural networks (DNNs) offer significant versatility and performance benefits, but their widespread adoption is often hindered by high model complexity and computational demands. Model compression techniques such as pruning have emerged as promising solutions to these challenges. However, it remains critical to ensure that application-specific performance characteristics are preserved during compression. In structured pruning, where groups of structurally coherent elements are removed, conventional importance metrics frequently fail to maintain these essential performance attributes. In this work, we propose an enhanced importance metric framework that not only reduces model size but also explicitly accounts for application-specific performance constraints. We employ multiple strategies to determine the optimal pruning magnitude for each group, ensuring a balance between compression and task performance. Our approach is evaluated on an autoencoder tasked with reconstructing MNIST images. Experimental results demonstrate that the proposed method effectively preserves task-relevant performance, maintaining the model's usability even after substantial pruning, by satisfying the required application-specific criteria.

Problem

• Balancing model compression and performance preservation
• Improving structured pruning with application-specific constraints
• Optimizing pruning magnitude for task-relevant performance

Innovation

• Soft coefficient optimization for structured pruning
• Application-specific component-aware importance metrics
• Balanced pruning magnitude for performance preservation