3 papers
cs.LG2025
Combining Relevance and Magnitude for Resource-Aware DNN Pruning
Carla Fabiana Chiasserini, Francesco Malandrino, Nuria Molner +1
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, espe…
cs.NI2025
Sharing GPUs and Programmable Switches in a Federated Testbed with SHARY
Stefano Salsano, Andrea Mayer, Paolo Lungaroni +8
Federated testbeds enable collaborative research by providing access to diverse resources, including computing power, storage, and specialized hardware like GPUs, programmable swit…
cs.AI2024
Resource-Efficient Sensor Fusion via System-Wide Dynamic Gated Neural Networks
Chetna Singhal, Yashuo Wu, Francesco Malandrino +3
Mobile systems will have to support multiple AI-based applications, each leveraging heterogeneous data sources through DNN architectures collaboratively executed within the network…