13 citations · 44 across the 4 of their papers we have counts for
4 papers
Robustifying the Deployment of tinyML Models for Autonomous mini-vehicles
Miguel de Prado, Manuele Rusci, Romain Donze +4
Standard-size autonomous navigation vehicles have rapidly improved thanks to the breakthroughs of deep learning. However, scaling autonomous driving to low-power systems deployed o…
Automated Design Space Exploration for optimised Deployment of DNN on Arm Cortex-A CPUs
Miguel de Prado, Andrew Mundy, Rabia Saeed +3
The spread of deep learning on embedded devices has prompted the development of numerous methods to optimise the deployment of deep neural networks (DNN). Works have mainly focused…
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…