Re:A Look at the AMD Zen 2 Core
by superpermutat0r on Saturday July 06, 2019 @12:39PM
(#20370465)
Hashed probably means that some features are mapped to a desired perceptron weight via a hash function. This serves as an implicit regularization and can be much more efficient (no need for a predetermined feature vector, sparse representation etc.). It's called hash trick in ML. Perceptron is a single layer NN. Dynamic predictor, a branch predictor that adapts to program input, not just some predetermined state (like a formula that was shown to be good enough for a collection of programs).
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In this case, I'm pretty sure it means that multiple instruction addreses are hashed to the same perceptron (or is what you said effectively the same thing in ML-speak).
Since you don't have branch predictors for each address, you share the same prediction data for multiple addresses (this is one often underlooked cost to heavily branchy code).