Stochastic hyperdimensional arithmetic computing

Abstract

Stochastic hyperdimensional arithmetic computing is provided. Hyperdimensional computing (HDC) is a neurally-inspired computation model working based on the observation that the human brain operates on high-dimensional representations of data, called hypervectors. Although HDC is powerful in reasoning and association of the abstract information, it is weak on feature extraction from complex data. Consequently, most existing HDC solutions rely on expensive pre-processing algorithms for feature extraction. This disclosure proposes StocHD, a novel end-to-end hyperdimensional system that supports accurate, efficient, and robust learning over raw data. StocHD expands HDC functionality to the computing area by mathematically defining stochastic arithmetic over HDC hypervectors. StocHD enables an entire learning application (including feature extractor) to process using HDC data representation, enabling uniform, efficient, robust, and highly parallel computation. This disclosure further provides a novel fully digital and scalable processing in-memory (PIM) architecture that exploits the HDC memory-centric nature to support extensively parallel computation.