Hyperdimensional Computing for Text Classification
Hyperdimensional computing explores the emulation of cognition by computing with hypervectors as an alternative to computing with numbers. Hypervectors are highdimensional, holographic, and (pseudo)random with i.i.d. components. These properties provide an opportunity for efficient computing. We focus on an application of hyperdimensional computing for text classification. Accordingly, we present an algorithm for classifying news articles from a stream of input letters. Using pentagrams of letters, the algorithm achieves a classification accuracy above 94% on eight news topics, surpassing other techniques reported in the literature, including Bayes, K NN, and SVM. We demonstrate a software framework that enables efficient execution of such algorithms on contemporary hardware fabrics.