22 citations · 46 across the 5 of their papers we have counts for
5 papers
Improving Continual Learning for Gaussian Splatting based Environments Reconstruction on Commercial Off-the-Shelf Edge Devices
Ivan Zaino, Matteo Risso, Daniele Jahier Pagliari +3
Novel view synthesis (NVS) is increasingly relevant for edge robotics, where compact and incrementally updatable 3D scene models are needed for SLAM, navigation, and inspection und…
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…
Bonseyes AI Pipeline -- bringing AI to you. End-to-end integration of data, algorithms and deployment tools
Miguel de Prado, Jing Su, Rabia Saeed +9
Next generation of embedded Information and Communication Technology (ICT) systems are collaborative systems able to perform autonomous tasks. The remarkable expansion of the embed…
Learning to infer: RL-based search for DNN primitive selection on Heterogeneous Embedded Systems
Miguel de Prado, Nuria Pazos, Luca Benini
Deep Learning is increasingly being adopted by industry for computer vision applications running on embedded devices. While Convolutional Neural Networks' accuracy has achieved a m…
QUENN: QUantization Engine for low-power Neural Networks
Miguel de Prado, Maurizio Denna, Luca Benini +1
Deep Learning is moving to edge devices, ushering in a new age of distributed Artificial Intelligence (AI). The high demand of computational resources required by deep neural netwo…