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  1. Abstract—Hyperdimensional Computing (HDC) is a neurallyinspired computation model working based on the observation that the human brain operates on high-dimensional representations of data, called hypervector. Although HDC is significantly powerful in reasoning and association of the abstract information, it is weak on features extraction from complex data such as image/video. As a result, most existing HDC solutions rely on expensive pre-processing algorithms for feature extraction. In 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 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 memorycentric nature to support extensively parallel computation. Our evaluation over a wide range of classification tasks shows that StocHD provides, on average, 3.3x and 6.4x (52.3x and 143.Sx) faster and higher energy efficiency as compared to state-of-the-art HDC algorithm running on PIM (NVIDIA GPU), while providing 16x higher computational robustness. 
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  2. Brain-inspired HyperDimensional Computing (HDC) is an alternative computation model working based on the observation that the human brain operates on highdimensional representations of data. Existing HDC solutions rely on expensive pre-processing algorithms for feature extraction. In this paper, we propose 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. 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. 
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