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

ArXi:2604.09988v1 Announce Type: cross 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.