faster than the software on this frame. (F) Energy versus time for software and hardware solutions to motion perception.]{}

![image](figures/f4-vision.ai){width="\textwidth"}

[**Figure 5.**]{} [(A) Example samples from the posterior distribution of cluster assignments for a nonparametric mixture model. The two samples show posterior variance, reflecting the uncertainty between three and four source clusters. (B) Typical handwritten digit images from the MNIST corpus[@lecun1998mnist], showing a high degree of variation across digits of the same type. (C) The digit clusters discovered automatically by a stochastic digital circuit for inference in Dirichlet process mixture models. Each image represents a cluster; each pixel represents the probability that the corresponding image pixel is black. Clusters are sorted according to the most probable true digit label of the images in the cluster. Note that these cluster labels were not provided to the circuit. Both the clusters and the number of clusters were discovered automatically by the circuit over the course of inference. (D) The receiver operating characteristic (ROC) curves that result from classifying digits using the learned clusters; quantitative results are competitive with state-of-the-art classifiers. (E) The time required for one cycle through the outermost transition circuit in hardware, versus the corresponding time for one sweep of 