260 citations · 809 across the 29 of their papers we have counts for
32 papers
Q-SHED: Distributed Optimization at the Edge via Hessian Eigenvectors Quantization
Nicolò Dal Fabbro, Michele Rossi, Luca Schenato +1
Edge networks call for communication efficient (low overhead) and robust distributed optimization (DO) algorithms. These are, in fact, desirable qualities for DO frameworks, such a…
Divide and Save: Splitting Workload Among Containers in an Edge Device to Save Energy and Time
Aria Khoshsirat, Giovanni Perin, Michele Rossi
The increasing demand for edge computing is leading to a rise in energy consumption from edge devices, which can have significant environmental and financial implications. To addre…
Energy Consumption of Neural Networks on NVIDIA Edge Boards: an Empirical Model
Seyyidahmed Lahmer, Aria Khoshsirat, Michele Rossi +1
Recently, there has been a trend of shifting the execution of deep learning inference tasks toward the edge of the network, closer to the user, to reduce latency and preserve data…
Energy Efficient Deployment and Orchestration of Computing Resources at the Network Edge: a Survey on Algorithms, Trends and Open Challenges
Neda Shalavi, Giovanni Perin, Andrea Zanella +1
Mobile networks are becoming energy hungry, and this trend is expected to continue due to a surge in communication and computation demand. Multi-access Edge Computing (MEC), will e…
Human Tracking with mmWave Radars: a Deep Learning Approach with Uncertainty Estimation
Jacopo Pegoraro, Michele Rossi
mmWave radars have recently gathered significant attention as a means to track human movement within indoor environments. Widely adopted Kalman filter tracking methods experience p…
SPARCS: A Sparse Recovery Approach for Integrated Communication and Human Sensing in mmWave Systems
Jacopo Pegoraro, Jesus Omar Lacruz, Michele Rossi +1
A well established method to detect and classify human movements using Millimeter-Wave ( mmWave) devices is the time-frequency analysis of the small-scale Doppler effect (termed mi…