Engineering Resource-constrained Software Systems with DNN Components: a Concept-based Pruning Approach
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 t...
| Authors | Federico Formica, Andrea Rota, Aurora Francesca Zanenga et al. |
|---|---|
| Primary category | cs.SE |
| Published | 11 April 2026 |
| Last updated at source | 11 April 2026 |
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