Recent advances in ultra-low-power microcontrollers and FPGAs together with the possibility of tailoring optimization algorithms and new machine learning techniques to such hardware make it possible to perform, on the edge, complex data analytics that were previously only possible on powerful computers. These techniques are especially relevant in applications such as planetary exploration missions where communication is not available in real-time and all computations should occur on-board. This project focuses on the following three areas:

Development of novel methods for embedded data analytics: Many applications in the space sciences or the internet of Things require the use of low-power devices. New research will be performed to develop new algorithms for the solution of optimization problems and machine learning techniques that are tailored to new hardware architectures. In particular, ultra-low-power microcontrollers and FPGAs will be studied.

Low-power and energy-aware data analytics: a co-design of the developed algorithms will be performed by analyzing performance and energy consumption. The goal is to provide optimal tradeoffs between performance and energy consumption, which can be adapted according to the current energy availability in different applications. Self-localization systems: when satellite-based systems are not available, being able to perform autolocalization is a critical task to any tasks that requires autonomous decision making as in planetary exploration missions. The developed methods will be applied and tailored for the challenging tasks usually encountered in self-localization systems for exploration missions.