Preprint Open access
Phase-HDC: Replacing Optimizer History with Gradient Thresholds in Discrete Phase Learning
Training a compact model often needs far more memory than storing it, because the optimizer keeps its own records of past gradients. For a hyperdimensional classifier whose learned parameters are low-bit angles, which we call a \emph{phase memory}, these records take several times more memory than the model itself. We …