in-memory compute

Analog noise breaks AI accuracy at a sharp threshold, not gradually

Analog hardware's promise hinges on a single question: how gracefully does it fail? This experiment shows the answer is not graceful at all. Accuracy holds steady, then collapses from 83% to random in what looks like a cliff, not a slope. That threshold behavior is worth pausing on. Injecting noise during training shifts the drop-off meaningfully, but the flat-minima explanation feels like a starting point, not the whole story. We'd love to see work that optimizes directly for the hardware's noise profile.