to economically exploit massive parallelism, by fitting many sampling units on a single chip.]{}

![image](figures/f3-multinomial-crop.pdf){width="\textwidth"}

[ (A) A Markov Random Field for solving depth and motion perception, as well as other dense matching problems. Each $X_{i,j}$ node stores the hidden quantity to be estimated, e.g. the disparity of a pixel. Each $f_{LP}$ ensures adjacent $X$s are either similar or very different, i.e. that depth and motion fields vary smoothly on objects but can contain discontinuities at object boundaries. Each $Y_{i,j}$ node stores a per-latent-pixel vector of similarity information for a range of candidate matches, linked to the $X$s by the $f_E$ potentials. (B) The conditional independencies in this model permit many different parallelization strategies, from fully space-parallel implementations to virtualized implementations where blocks of pixels are updated in parallel. (C) Depth perception results. The left input image, plus the depth maps obtained by software (middle) and hardware (right) engines for solving the Markov Random Field. (D) Motion perception results. One input frame, plus the motion flow vector fields for software (middle) and hardware (right) solutions. (E) Energy versus time for software and hardware solutions to depth perception, including both 8-bit and 12-bit hardware. Note that the hardware is roughly 500x 