1 citations · 1 across the 7 of their papers we have counts for
7 papers
Multi-resolution Rescored ByteTrack for Video Object Detection on Ultra-low-power Embedded Systems
Luca Bompani, Manuele Rusci, Daniele Palossi +2
This paper introduces Multi-Resolution Rescored Byte-Track (MR2-ByteTrack), a novel video object detection framework for ultra-low-power embedded processors. This method reduces th…
On-Device Domain Learning for Keyword Spotting on Low-Power Extreme Edge Embedded Systems
Cristian Cioflan, Lukas Cavigelli, Manuele Rusci +2
Keyword spotting accuracy degrades when neural networks are exposed to noisy environments. On-site adaptation to previously unseen noise is crucial to recovering accuracy loss, and…
On-device Self-supervised Learning of Visual Perception Tasks aboard Hardware-limited Nano-quadrotors
Elia Cereda, Manuele Rusci, Alessandro Giusti +1
Sub-\SI{50}{\gram} nano-drones are gaining momentum in both academia and industry. Their most compelling applications rely on onboard deep learning models for perception despite se…
Land & Localize: An Infrastructure-free and Scalable Nano-Drones Swarm with UWB-based Localization
Mahyar Pourjabar, Ahmed AlKatheeri, Manuele Rusci +5
Relative localization is a crucial functional block of any robotic swarm. We address it in a fleet of nano-drones characterized by a 10 cm-scale form factor, which makes them highl…
Few-Shot Open-Set Learning for On-Device Customization of KeyWord Spotting Systems
Manuele Rusci, Tinne Tuytelaars
A personalized KeyWord Spotting (KWS) pipeline typically requires the training of a Deep Learning model on a large set of user-defined speech utterances, preventing fast customizat…
Reduced Precision Floating-Point Optimization for Deep Neural Network On-Device Learning on MicroControllers
Davide Nadalini, Manuele Rusci, Luca Benini +1
Enabling On-Device Learning (ODL) for Ultra-Low-Power Micro-Controller Units (MCUs) is a key step for post-deployment adaptation and fine-tuning of Deep Neural Network (DNN) models…