Probabilistic Computing for Efficient Robotic Vision in Space
Computation can be made more energy efficient by allowing a larger chance for erroneous results. Can we make vision algorithms that exploit such probabilistic computing?
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.
1.2 Software – efficient and robust algorithms
Visual attention models could form a successful strategy for reducing the
computational effort of computer vision algorithms. The central idea of visual
attention models is that they devote most of the processing to the most relevant parts
of the image. Surely, the main challenge is to automatically determine what is
`relevant'. Several models have been devised that can determine relevant parts of an
image, but many of them extensively process the entire image for determining regions
of interest, inadvertently still leading to a large computational burden [1, 2, 3].
Figure 1: Number of samples extracted from an image versus average absolute errors in the estimates of the pitch and roll angle. The solid lines are for the results in which a linear algebraic solution is used for estimating the horizon (LA), the dashed lines are for the results with a linear perceptron.
1.3 Hardware – probabilistic computing
If one accepts that vision algorithms can gain in efficiency by using only part of the information in an image, one can extend this reasoning to the hardware involved in the calculations to save even more time / energy. In typical processor hardware considerable amounts of energy are spent on obtaining correct calculation results, e.g., for adding or multiplying numbers. In probabilistic computing [12,13,14], the energy spent by the processing units is lowered, resulting in an increase of the probability that some operations might go wrong. Fortunately, it has been shown that the amount of energy saved is significantly larger than the amount of probability traded in  - see the figure below. As a consequence, in theory, large energy gains (in the order of 5 times) can be obtained at a minimal cost in calculation errors.
Figure 2: Energy expenditure versus the probability p that calculations will be incorrect. Figure adopted from .
Different applications of probabilistic computing have been investigated. The study in
 focused on the application of probabilistic computing to the calculation of the
Fourier transform, showing that for human subjects the probabilistic reconstruction is
visually identical to the error-free one. Another study involved the application of
probabilistic computing to Continuous Restricted Boltzmann Machines (CBRM) .
Such CBRMs normally require the generation of pseudo-random numbers. Instead, in
 probabilistic computing naturally provided random numbers.
2012-01 - The study: "Probabilistic computing for efficient robotic vision in space" will officially start in the course of 2012.
 L. Itti, C. Koch, E. Niebur, A model of saliency-based visual attention for rapid scene analysis, IEEE Transactions on Pattern Analysis and Machine Intelligence 20 (11) (1998) 1254 - 1259.