output
20182022
most citedBonseyes AI Pipeline -- bringing AI to you. End-to-end integration of data, algorithms and deployment tools

22 citations

researchers with a paper here

Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.

collaborating institutions

5 papers

q-bio.QM2022★ 6 cited

Flow Rate Independent Multiscale Liquid Biopsy for Precision Oncology

Jing Yan, Jie Wang, Robert Dallmann +2

Immunoaffinity-based liquid biopsies of circulating tumor cells (CTCs) hold great promise for cancer management, but typically suffer from low throughput, relative complexity and p…

cs.CV2020★ 9 cited

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…

cs.LG2020★ 11 cited

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…

cs.LG2019★ 22 cited

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…

cs.CV2018★ 11 cited

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…