Internship: Development of a prototype HW platform for embedded object detection with bio-inspired retinas


The LEAT lab is leader of the national ANR project DeepSee in collaboration with Renault, Prophesee and 2 other labs in neuroscience (CERCO) and computer science (I3S). This project aims at exploring a bio-inspired approach to develop energy-efficient solutions for image processing in automotive applications (ADAS) as explored by [3]. The main mechanisms that are used to follow this approach are event-based cameras (EBC are considered as artificial retinas) and spiking neural networks (SNN).
The first one is a type of sensor detecting the change of luminosity at very high temporal resolution and low power consumption, the second one is a type of artificial neural network mimicking the way the information is encoded in the brain. The LEAT has developed the first model of SNN able to make object detection on event-based data [1] and the related hardware accelerator on FPGA [2]. The goal of this internship project is to deploy this spike-based AI solution onto an embedded smart camera provided by the Prophesee company [4]. The camera is composed of an event-based sensor and an FPGA. The work will mainly consist in deploying the existing software code (in C) on the embedded CPU, integrate the HW accelerator (VHDL) onto the FPGA and make the communication between them through an AXI-STREAM bus. The last part of the project will consist in realizing experimentations of the resulting smart cameras to evaluate the real-time performances and energy consumption before a validation onto a driving vehicle.

Project mission

The project mission will be organized in several periods:

  • Bibliographic study on event-based processing
  • Introduction to the existing Sw and Hw solutions at LEAT, and to the dev kit from Prophesee
  • Deployment of the Sw part on CPU and the Hw part on FPGA
  • Experimentations and validation
  • Publication in an international conference.


[1] L Cordone, B Miramond, P Thierion, Object Detection with Spiking Neural Networks on Automotive Event
Data, IEEE International Joint Conference on Neural Networks (IJCNN), 2022
[2] N Abderrahmane, B Miramond, E Kervennic, A Girard, SPLEAT: SPiking Low-power Event-based ArchiTecture
for in-orbit processing of satellite imagery, IEEE International Joint Conference on Neural Networks, 1-10, 2022
[3] G. Chen et al, Event-based neuromorphic vision for autonomous driving: A paradigm shift for bio-inspired
visual sensing and perception, IEEE Signal Processing Magazine, 2020
[4] P. De Tournemire et al, A Large Scale Event-based Detection Dataset for Automotive, 2020

Practical information

Location : LEAT Lab / SophiaTech Campus, Sophia Antipolis
Duration : 6 months from march 2023
Grant : from ANR project DeepSee
Profile : VHDL programming, FPGA design, C programming, signal/image processing
Research keywords : Embedded systems, Event-based camera, artificial neural network, Edge AI

Contact and supervision

Benoît Miramond, Edgar Lemaire
LEAT Lab – University Cote d’Azur / CNRS
Polytech Nice Sophia /