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Course I: Accelerated deployment of DNNs on edge devices


About This Course

This course aims to provide an overview of the deep learning concepts, processes, and workflow that are available at the Bonseyes Marketplace to deploy deep neural networks (DNNs) on edge devices.

The course starts with a general overview of deep learning concepts, helping the user to install the right software environment. The course then deepens into two main workflows, namely, Develop Platforms and AI Assets, where the user needs to reproduce the given examples on an embedded platform.



  • Basic understanding of deep learning
  • Basic understanding of embedded devices
  • PC/laptop
  • Raspberry Pi 4b (embedded platform)

Course Staff

Course Staff Image #1

Ivi Prifti

Ivi completed his master's degree at the Technical University of Kaiserslautern in 2019. He likes to interact with platforms and cutting-edge sensor technologies, develop algorithms to control and interconnect them. This passion motivated him to find a part-time job in research, where he continuously faced challenging problems, but he also learned how to divide these problems into smaller steps and conquer the challenges one by one. His fields of interest are Digital Design(RTL), as well as Firmware development for FPGAs and microcontrollers.

Course Staff Image #2

Miguel de Prado Escudero

Miguel de Prado Escudero was born on the 3rd of January of 1991 in Madrid. He received his B.Sc in Electronic Engineering and Automation in 2013 at the University Carlos III de Madrid (Spain). He received a scholarship awarded by the European Commission to pursue a European M.Sc in Embedded Computing Systems at the University of Southampton, UK (1st class distinction) and TU Kaiserslautern, Germany (highest grade in M.Sc. thesis) from 2014 to 2016. He obtained his PhD on automation for optimised and robust deployment of neural networks on edge devices at ETH Zürich under Prof. Luca Benini and Prof. Nuria Pazos. He has been working at the EU-H2020 Bonseyes/BonsAPPs project since 2017 to democratise the use of AI on embedded devices by collaborating with several institutions towards the construction of a collaborative European AI Marketplace. Miguel de Prado’s research work investigates several research questions towards the automation, exploration and, adaptation of DNNs deployed on edge devices.

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