most citedHuman Activity Recognition on Microcontrollers with Quantized and Adaptive Deep Neural Networks

52 citations · 57 across the 3 of their papers we have counts for

collaborators

13 papers

cs.CV20241 cited

Building Damage Assessment in Conflict Zones: A Deep Learning Approach Using Geospatial Sub-Meter Resolution Data

Matteo Risso, Alessia Goffi, Beatrice Alessandra Motetti +6

Very High Resolution (VHR) geospatial image analysis is crucial for humanitarian assistance in both natural and anthropogenic crises, as it allows to rapidly identify the most crit…

eess.SP2024

Optimization and Deployment of Deep Neural Networks for PPG-based Blood Pressure Estimation Targeting Low-power Wearables

Alessio Burrello, Francesco Carlucci, Giovanni Pollo +5

PPG-based Blood Pressure (BP) estimation is a challenging biosignal processing task for low-power devices such as wearables. State-of-the-art Deep Neural Networks (DNNs) trained fo…

cs.LG2024

Optimizing the Deployment of Tiny Transformers on Low-Power MCUs

Victor J. B. Jung, Alessio Burrello, Moritz Scherer +2

Transformer networks are rapidly becoming SotA in many fields, such as NLP and CV. Similarly to CNN, there is a strong push for deploying Transformer models at the extreme edge, ul…

cs.AR20242 cited

Performance evaluation of acceleration of convolutional layers on OpenEdgeCGRA

Nicolò Carpentieri, Juan Sapriza, Davide Schiavone +4

Recently, efficiently deploying deep learning solutions on the edge has received increasing attention. New platforms are emerging to support the increasing demand for flexibility a…

cs.CV2024

Optimized Deployment of Deep Neural Networks for Visual Pose Estimation on Nano-drones

Matteo Risso, Francesco Daghero, Beatrice Alessandra Motetti +4

Miniaturized autonomous unmanned aerial vehicles (UAVs) are gaining popularity due to their small size, enabling new tasks such as indoor navigation or people monitoring. Nonethele…

cs.CV20241 cited

Adaptive Deep Learning for Efficient Visual Pose Estimation aboard Ultra-low-power Nano-drones

Beatrice Alessandra Motetti, Luca Crupi, Mustafa Omer Mohammed Elamin Elshaigi +4

Sub-10cm diameter nano-drones are gaining momentum thanks to their applicability in scenarios prevented to bigger flying drones, such as in narrow environments and close to humans.…