no code implementations • 1 Jul 2020 • Miguel de Prado, Manuele Rusci, Romain Donze, Alessandro Capotondi, Serge Monnerat, Luca Benini and, Nuria Pazos
We leverage a family of compact and high-throughput tinyCNNs to control the mini-vehicle, which learn in the target environment by imitating a computer vision algorithm, i. e., the expert.
no code implementations • 9 Jun 2020 • Miguel de Prado, Andrew Mundy, Rabia Saeed, Maurizio Denna, Nuria Pazos, Luca Benini
The framework relies on a Reinforcement Learning search that, combined with a deep learning inference framework, automatically explores the design space and learns an optimised solution that speeds up the performance and reduces the memory on embedded CPU platforms.
no code implementations • 15 Jan 2019 • Miguel de Prado, Jing Su, Rabia Saeed, Lorenzo Keller, Noelia Vallez, Andrew Anderson, David Gregg, Luca Benini, Tim Llewellynn, Nabil Ouerhani, Rozenn Dahyot and, Nuria Pazos
In this work, we present a modular AI pipeline as an integrating framework to bring data, algorithms, and deployment tools together.
no code implementations • 18 Nov 2018 • Miguel de Prado, Nuria Pazos, Luca Benini
In this work, we present QS-DNN, a fully automatic search based on Reinforcement Learning which, combined with an inference engine optimizer, efficiently explores through the design space and empirically finds the optimal combinations of libraries and primitives to speed up the inference of CNNs on heterogeneous embedded devices.
no code implementations • 14 Nov 2018 • Miguel de Prado, Maurizio Denna, Luca Benini, Nuria Pazos
Deep Learning is moving to edge devices, ushering in a new age of distributed Artificial Intelligence (AI).