FNR BRIDGES BrainSatCom

FNR Bridges BrainSatCom: Brain-inspired Neuromorphic Computing Techniques to Energy-Efficient Satellite Communications 

Project Description:

Satellites are crucial for everything from weather forecasting to global communications, but they face significant challenges, such as limited power supply and the need to make quick decisions in constantly changing space conditions. The BrainSatCom project addresses these challenges by introducing an innovative approach called neuromorphic computing to satellite communications. Neuromorphic computing is inspired by the human brain. It uses special types of processors, which are like tiny brains, to handle tasks much more efficiently than traditional computer systems. These processors, which use what we call spiking neural networks, mimic the way neurons in the human brain communicate with each other. This method is not only faster, but also consumes much less power.

The BrainSatCom project, conducted in collaboration with SES, a company that operates satellite communications, focuses on testing and improving these brain-like processors to make satellites more efficient. The project includes:

1. Comparing the power consumption of traditional satellite systems with that of the brain-inspired systems.

2. Perform simulations and hardware experiments with these advanced processors to test their performance.

3. Create and refine these models to process real-time data onboard satellites.

By making satellites smarter and more efficient, neuromorphic computing could transform satellite communications. This could make satellite services faster and more reliable, and even help to better manage the limited energy resources of space. The success of this project could set a new standard for future satellite operations, making them more capable and efficient.

Duration:

2 years (starting 01.10.2024)

Funding:

This project is funded by the Luxembourg National Research Fund (FNR) under the BRIDGES program in the 2024 call.

Luxembourg Partner: SES Luxembourg

Research Team:

• Prof. Symeon Chatzinotas (PI)

• Dr. Flor Ortiz (SnT) - VPI

• Dr. Eva Lagunas (SnT)

• Suleima Briseno (SnT)

• Raudel Cuiman (SnT)

• Madyan Al-Senwi (SnT)

• Abuzar B.M. Adam

• Sirine Ben Ati - PhD Student