Noise-shaped Sampling and Annealing for Scalable On-the-edge Training of Binary and Ternary AI Models

The training loss of binary and ternary neural networks, expanded as a multilinear polynomial over the discrete weights, is structurally equivalent to the energy of a hierarchical high-order Ising model.