Simple Encoder Training for Hyperdimensional Computing

Abstract

Hyperdimensional computing (HDC) is gaining popularity as a lightweight alternative computing paradigm, where data are represented as long vectors (commonly of dimension 10,000) and processed using simple algebraic operations. In the two-part HDC classification pipeline, data are first encoded as a high-dimensional representation, then classified based on their similarity to a set of prototypes. Conventionally, the prototypes are trained while the encoder remains static. In this work, we propose a method of training a binary/bipolar HDC encoder, relying exclusively on native HDC integer and binary operations. Evaluations with our method show an average accuracy improvement of 2.13% over the untrained encoder on several classification data sets commonly used in HDC research, with no increase in model size or inference complexity.