SearcHD: A Memory-Centric Hyperdimensional Computing With Stochastic Training

Abstract

Brain-inspired hyperdimensional (HD) computing emulates cognitive tasks by computing with long binary vectors—also know as hypervectors—as opposed to computing with numbers. However, we observed that in order to provide acceptable classification accuracy on practical applications, HD algorithms need to be trained and tested on nonbinary hypervectors. In this article, we propose SearcHD, a fully binarized HD computing algorithm with a fully binary training. SearcHD maps every data points to a high-dimensional space with binary elements. Instead of training an HD model with nonbinary elements, SearcHD implements a full binary training method which generates multiple binary hypervectors for each class. We also use the analog characteristic of nonvolatile memories (NVMs) to perform all encoding, training, and inference computations in memory. We evaluate the efficiency and accuracy of SearcHD on a wide range of classification applications. Our evaluation shows that SearcHD can provide on average $31.1\times $ higher energy efficiency and $12.8\times $ faster training as compared to the state-of-the-art HD computing algorithms.