flavio-ponzina/microhd

This repository provides two command-line experiments for training and resource-aware optimization of hyperdimensional classifiers: one uses ID-level encoding and the other projection encoding. Each selects and prepares a dataset, trains and evaluates a baseline, then searches encoding dimensions and quantization settings (plus levels for ID-level) under accuracy-drop thresholds, reporting estimated memory and bit operations. Shared encoders, classifier, training/evaluation routines, and requirement calculations live in utils.py. Edges follow explicit calls in the excerpts; external dataset sources are shown as inputs, not as inspected integrations.