298 citations · 577 across the 14 of their papers we have counts for
4 papers · 1 filter
A Multi-task Supervised Compression Model for Split Computing
Yoshitomo Matsubara, Matteo Mendula, Marco Levorato
Split computing ( split learning) is a promising approach to deep learning models for resource-constrained edge computing systems, where weak sensor (mobile) devices are wire…
Supervised Compression for Resource-Constrained Edge Computing Systems
Yoshitomo Matsubara, Ruihan Yang, Marco Levorato +1
There has been much interest in deploying deep learning algorithms on low-powered devices, including smartphones, drones, and medical sensors. However, full-scale deep neural netwo…
Split Computing for Complex Object Detectors: Challenges and Preliminary Results
Yoshitomo Matsubara, Marco Levorato
Following the trends of mobile and edge computing for DNN models, an intermediate option, split computing, has been attracting attentions from the research community. Previous stud…
Neural Compression and Filtering for Edge-assisted Real-time Object Detection in Challenged Networks
Yoshitomo Matsubara, Marco Levorato
The edge computing paradigm places compute-capable devices - edge servers - at the network edge to assist mobile devices in executing data analysis tasks. Intuitively, offloading c…