StocHD: Stochastic Hyperdimensional System for Efficient and Robust Learning from Raw Data
Hosted in Virtual Platform
SoC, Heterogeneous, and Reconfigurable Architectures
DescriptionIn this paper, we propose StocHD, a novel end-to-end hyperdimensional system that supports accurate, efficient, and robust learning over raw data. Unlike prior work that used HDC for learning tasks, StocHD expands HDC functionality to the computing area by mathematically defining stochastic arithmetic operations 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. We also propose a novel fully digital and scalable Processing In-Memory (PIM) architecture that exploits the HDC memory-centric nature to support extensively parallel computation.