My understanding is that they run a code that does 2000 branches based on a pseudo-random sequence. Over around 10 runs of that code, the CPU supposedly learns to correctly predict those 2000 branches and the performance steadily increases.
Do the modern branch predictors really have the capability to remember an exact sequence of past 2000 decisions on the same branch instruction? Also, why would the performance increase incrementally like that? I would imagine that it would remember the loop history on the first run and achieve maximum performance on the second run.
I doubt that there's really a neural net in the silicon doing this as the author speculates.