An approach to self-learning multicore reconfiguration management applied on Robotic Vision

Robotic Vision combined with real-time control imposes challenging requirements on embedded computing nodes in robots, exhibiting strong variations in computational load due to dynamically changing activity profiles. Reconfigurable Multiprocessor System-on-Chip offers a solution by efficiently handling the robot's resources, but reconfiguration management seems challenging. The goal of this paper is to present first ideas on self-learning reconfiguration management for Reco nfigurable multicore computing nodes with dynamic reconfiguration of soft-core CPUs and HW accelerators, to support dynamically changing activity profiles in Robotic Vision scenarios.