654 citations · 1.5k across the 37 of their papers we have counts for
9 papers · 1 filter
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…
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…
BottleFit: Learning Compressed Representations in Deep Neural Networks for Effective and Efficient Split Computing
Yoshitomo Matsubara, Davide Callegaro, Sameer Singh +2
Although mission-critical applications require the use of deep neural networks (DNNs), their continuous execution at mobile devices results in a significant increase in energy cons…
Federated Deep Reinforcement Learning for the Distributed Control of NextG Wireless Networks
Peyman Tehrani, Francesco Restuccia, Marco Levorato
Next Generation (NextG) networks are expected to support demanding tactile internet applications such as augmented reality and connected autonomous vehicles. Whereas recent innovat…