Engineering Resource-constrained Software Systems with DNN Components: a Concept-based Pruning Approach

2026-04-11

AI Summary

This work addresses the computational, memory, and storage bottlenecks associated with deploying large-scale deep neural networks (DNNs) in resource-constrained environments. The authors propose a novel pruning method that integrates system-level engineering requirements with human-interpretable concepts—such as color and semantic categories—to identify critical neurons through analysis of their activation patterns, thereby guiding the generation of lightweight models. Notably, this approach is the first to incorporate interpretable concepts directly into the DNN pruning pipeline. Evaluated on VGG-19 using a dataset comprising 26,384 RGB images, the method yields pruned models that achieve substantial reductions in model size and computational overhead while maintaining high performance, demonstrating strong applicability across diverse real-world scenarios with stringent resource constraints.

Technology Category

• Machine Learning: Learning on the Edge & Model Compression
• Computer Vision: Interpretability, Explainability, and Transparency
• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models

Application Category

• Web Mining and Content Analysis: Large pretrained models with web data
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

Abstract

Deep Neural Networks (DNNs) are widely used by engineers to solve difficult problems that require predictive modeling from data. However, these models are often massive, with millions or billions of parameters, and require substantial computational power, RAM, and storage. This becomes a limitation in practical scenarios where strict size and resource constraints must be respected. In this paper, we present a novel concept-based pruning technique for DNNs that guides pruning decisions using human-interpretable concepts, such as features, colors, and classes. This is particularly important in a software engineering context, as DNNs are integrated into systems and must be pruned according to specific system requirements. Our concept-based pruning solution analyzes neuron activations to identify important neurons from a system requirements viewpoint and uses this information to guide the DNN pruning. We assess our solution using the VGG-19 network and a dataset of 26'384 RGB images, focusing on its ability to produce small, effective pruned DNNs and on the computational complexity and performance of these pruned DNNs. We also analyzed the pruning efficiency of our solution and compared alternative configurations. Our results show that concept-based pruning efficiently generates much smaller, effective pruned DNNs. Pruning greatly improves the computational efficiency and performance of DNNs, properties that are particularly useful for practical applications with stringent memory and computational time constraints. Finally, alternative configuration options enable engineers to identify trade-offs adapted to different practical situations.

Problem

• resource-constrained systems
• Deep Neural Networks
• model pruning
• software engineering
• system requirements

Innovation

• concept-based pruning
• resource-constrained systems
• interpretable concepts
• neuron activation analysis
• DNN compression