654 citations · 1.5k across the 37 of their papers we have counts for
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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…
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…