Probabilistic Computing for Efficient Robotic Vision in Space
Computation can be made more energy efficient by allowing a larger chance for erroneous results. We have developed vision algorithms that exploit such probabilistic computing.
Study Description
In space robotics, the computational efficiency of algorithms is at a prime: tasks such as autonomous landing or rover navigation require a quick and efficient determination of actions, given an amount of energy and processing capacity that is much more restricted than in earth-based scenarios.
In this study we develop an integrated software and hardware approach for reducing the computational effort and energy expenditure of computer vision algorithms. It is based on local sampling on the software side and probabilistic computing on the hardware side.
Specifically, on the software side, we study random local sampling techniques that reduce the number of necessary computations at the cost of a slightly lower accuracy. On the hardware side, a research group with expertise in probabilistic computing has studied such computing as a means of saving energy at the cost of occasional errors in the calculations.