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Related Experiment Video

Updated: Jul 24, 2025

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
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Implementation of a Bio-Inspired Neural Architecture for Autonomous Vehicles on a Multi-FPGA Platform.

Tarek Elouaret1,2, Sylvain Colomer1,2, Frédéric De Melo1

  • 1Laboratoire ETIS, CY Cergy-Paris Université, ENSEA, CNRS, 95000 Cergy-Pontoise, France.

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|July 11, 2023
PubMed
Summary

This study presents an energy-efficient hardware solution for autonomous vehicle visual localisation using Field-Programmable Gate Arrays (FPGAs). The proposed system significantly reduces latency and power consumption compared to software-based methods.

Keywords:
FPGAGTX transceiversN-LOCNvidia Jetson TX2Wizarde custom platformbio-inspired algorithmshardware accelerationneural networks

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Area of Science:

  • Computer Engineering
  • Robotics
  • Artificial Intelligence

Background:

  • Autonomous vehicles rely on accurate self-localisation, with cameras being cost-effective sensors.
  • Visual localisation demands high computational power, real-time processing, and energy efficiency.
  • Field-Programmable Gate Arrays (FPGAs) offer a platform for energy-efficient hardware acceleration.

Purpose of the Study:

  • To propose and evaluate a distributed, hardware-based solution for a bio-inspired visual localisation model.
  • To assess the energy efficiency and performance improvements of an FPGA implementation over software solutions.

Main Methods:

  • Developed an image processing Intellectual Property (IP) core for landmark detection.
  • Implemented the N-LOC bio-inspired neural architecture on an FPGA.
  • Created a distributed N-LOC version for single and multi-FPGA platforms.

Main Results:

  • The hardware-based IP implementation achieved up to 9x lower latency and 7x higher throughput.
  • The system demonstrated significant energy efficiency, with a power footprint as low as 2.741 W.
  • Power consumption was 5.5-6x lower than the Nvidia Jetson TX2.

Conclusions:

  • The proposed distributed FPGA solution offers a promising approach for energy-efficient visual localisation.
  • Hardware acceleration on FPGAs significantly enhances performance and reduces power consumption for autonomous driving systems.