654 citations · 1.5k across the 37 of their papers we have counts for
38 papers
Slimmable Encoders for Flexible Split DNNs in Bandwidth and Resource Constrained IoT Systems
Juliano S. Assine, J. C. S. Santos Filho, Eduardo Valle +1
The execution of large deep neural networks (DNN) at mobile edge devices requires considerable consumption of critical resources, such as energy, while imposing demands on hardware…
Active Reinforcement Learning for Personalized Stress Monitoring in Everyday Settings
Ali Tazarv, Sina Labbaf, Amir Rahmani +2
Most existing sensor-based monitoring frameworks presume that a large available labeled dataset is processed to train accurate detection models. However, in settings where personal…
Matching DNN Compression and Cooperative Training with Resources and Data Availability
Francesco Malandrino, Giuseppe Di Giacomo, Armin Karamzade +2
To make machine learning (ML) sustainable and apt to run on the diverse devices where relevant data is, it is essential to compress ML models as needed, while still meeting the req…
Edge Learning for B5G Networks with Distributed Signal Processing: Semantic Communication, Edge Computing, and Wireless Sensing
Wei Xu, Zhaohui Yang, Derrick Wing Kwan Ng +3
To process and transfer large amounts of data in emerging wireless services, it has become increasingly appealing to exploit distributed data communication and learning. Specifical…
SC2 Benchmark: Supervised Compression for Split Computing
Yoshitomo Matsubara, Ruihan Yang, Marco Levorato +1
With the increasing demand for deep learning models on mobile devices, splitting neural network computation between the device and a more powerful edge server has become an attract…
SmartDet: Context-Aware Dynamic Control of Edge Task Offloading for Mobile Object Detection
Davide Callegaro, Francesco Restuccia, Marco Levorato
Mobile devices increasingly rely on object detection (OD) through deep neural networks (DNNs) to perform critical tasks. Due to their high complexity, the execution of these DNNs r…