Artificial Intelligence has gained widespread adoption across different industrial sectors, serving as a versatile tool to carry out a diverse array of tasks, ranging from image classification and traffic forecasting to natural language processing and speech recognition. In the space domain, however, a special focus must be placed on area overhead, power consumption, and fault-tolerant solutions. In this particular scenario, soft General-Purpose Computing on Graphic Processing Units has the potential to revolutionise space-related activities. Indeed, by leveraging both Field Programmable Gate Array technology and Graphic Processing Unit computing, it becomes feasible to achieve high-performance capabilities without compromising neither power consumption nor radiation tolerance features. Moreover, the use of reconfigurable hardware can facilitate the acceleration of a wide range of Machine Learning algorithms, avoiding the drawbacks of excessive specialisation. This paper explores the State-of-the-Art in terms of hardware platforms for on-the-edge acceleration of Artificial Intelligence algorithms and compares it with a possible System-on-Chip implementation based on a soft-Graphic Processing Unit. Then, the attention is shifted towards the investigation of key aspects for future space missions, such as reliability and Dynamic Partial Reconfiguration. We point out the lack of European technological solutions, emphasising the promising potential offered by NanoXplore devices. We also discuss the importance of fault detection and mitigation techniques in space applications, covering the most commonly employed hardware methods for reliability enhancement and highlighting the lack of work in the field of General-Purpose Computing for Graphic Processing Units, especially in the space sector. Furthermore, we briefly examine the implementation of Dynamic Partial Reconfiguration over a System-on-Chip featuring a soft-Graphic Processing Unit IP. Finally, in the last section of the paper, we hint at future development of the project and conclude the work.

Keywords: Soft GPGPU, On-board Acceleration, Artificial Intelligence, Space Applications, System-on-Chip, Reliability, Dynamic Partial Reconfiguration