we also show architectures that exhibit minimal degradation of accuracy in the presence of fault rates as high as one bit error for every 100 state transitions, in contrast to conventional architectures where failure rates are measured in bit errors (failures) per billion hours of operation[@bertrillions].

Our first application is to depth and motion perception, via Bayesian inference in lattice Markov Random Field models[@gemanstochastic]. The core problem is matching pixels from two images of the same scene, taken at distinct but nearby points in space or in time. The matching is ambiguous on the basis of the images alone, as multiple pixels might share the same value[@marrcooperative]; prior knowledge about the structure of the scene must be applied, which is often cast in terms of Bayesian inference[@szeliskicomparative]. Figure 4A illustrates the template probabilistic model most commonly used. The X variables contain the unknown displacement vectors. Each Y variable contains a vector of pixel similarity measurements, one per possible pair of matched pixels based on X. The pairwise potentials between the X variables encode scene structure assumptions; in typical problems, unknown values are assumed to vary smoothly across the scene, with a small number of discontinuities at the boundaries of objects. 