As the Moon reemerges as a renewed fronteer in space exploration, the Lunar Zebro project proposes to deploy a swarm of miniature rovers for efficient lunar surface exploration. One of their goals is to leverage recent advancements in deep learning and AI-accelerating hardware, in conjunction with Commercial Off-The-Shelf technologies and the NewSpace movement, to enhance the autonomous capabilities of these nano-rovers. This research focuses on integrating AI-accelerating hardware within the stringent Size, Weight, and Power (SWaP) constraints of these lunar rovers. It evaluates the suitability of various hardware configurations. A Convolutional Neural Network for hazard detection was trained and tested on different devices and scenarios. Finally, the operational cycle of the rover was simulated and the constrained resources were tracked for the different design options.