"Backpropagation, the training algorithm used almost exclusively in AI systems today, is incompatible with analog hardware since it is sensitive to the small variabilities and mismatches in on-chip analog devices. While compensation techniques have been used to make analog inference chips, these techniques have yet to prove successful for backpropagation-based training. Rain’s approach, which uses activity difference techniques, calculates local gradients instead of backpropagation’s repeated use of global gradients. The technique builds on previous work on equilibrium propagation training algorithms and is mathematically equivalent to backpropagation; in other words, it can be used to train mainstream deep learning networks."