Combining Relevance and Magnitude for Resource-saving DNN Pruning
Artikel i vetenskaplig tidskrift, 2025
Pruning neural networks, i.e., removing some of their parameters whilst retaining their accuracy, is one of the main ways to reduce the latency of a machine learning pipeline, especially in resource- and/or bandwidth-constrained scenarios. In this context, the pruning technique, i.e., how to choose the parameters to remove, is critical to the system performance. In this paper, we propose a novel pruning approach, called FlexRel and predicated upon combining training-time and inference-time information, namely, parameter magnitude and relevance, in order to improve the resulting accuracy whilst saving both computational resources and bandwidth. Our performance evaluation shows that FlexRel is able to achieve higher pruning factors, saving over 35% bandwidth for typical accuracy targets.
Machine Learning model compression
Resource utilization
Distributed learning
Författare
Carla Fabiana Chiasserini
Politecnico di Torino
Nätverk och System
Consiglo Nazionale Delle Richerche
Consorzio Nazionale Interuniversitario per le Telecomunicazioni (CNIT)
Francesco Malandrino
Consorzio Nazionale Interuniversitario per le Telecomunicazioni (CNIT)
Consiglo Nazionale Delle Richerche
N. Molner
Instituto de Telecomunicaciones y Aplicaciones Multimedia
Z. Zhao
Politecnico di Torino
IEEE Network
0890-8044 (ISSN) 1558156x (eISSN)
Vol. In PressÄmneskategorier (SSIF 2025)
Datavetenskap (datalogi)
DOI
10.1109/MNET.2025.3556212