Methods of providing trained hyperdimensional machine learning models having classes with reduced elements and related computing systems

A method of generating a sparsified non-binary hyperdimensional machine learning model configured for classification using a sparse-data memory structure, the method comprising:

• accessing, by one or more processor circuits, a trained non-binary hyperdimensional machine learning model stored in memory, the trained non-binary hyperdimensional machine learning model comprising a plurality of class hypervectors, each class hypervector comprising a plurality of numeric-valued elements at respective index positions;
• computing, by the one or more processor circuits, a variation metric for each index position, the variation metric representing a statistical difference among the numeric-valued elements at that index position across the plurality of class hypervectors, the computed variation metrics forming a set of variation metric values respectively associated with the index positions;
• evaluating, by the one or more processor circuits, the index positions based at least in part on the set of variation metric values;
• identifying, by the one or more processor circuits, a subset of the index positions selected based at least in part on the set of variation metric values;
• modifying the trained non-binary hyperdimensional machine learning model by assigning a zero value to each numeric-valued element located at each index position in the identified subset, across each of the class hypervectors;
• and
• outputting a sparsified trained non-binary hyperdimensional machine learning model comprising the plurality of class hypervectors including zero-valued elements at the index positions in the identified subset, the sparsified trained non-binary hyperdimensional machine learning model including a structure that enables compression or skipping of the zero-valued elements during memory access or computation in classification operations.