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Stochastic computing (SC) is a re-emerging computing paradigm providing low-cost and noise-tolerant designs for a wide range of arithmetic operations. SC circuits operate on uniform bit-streams with the value determined by the probability of observing 1’s in the bit-stream. The accuracy of SC operations highly depends on the correlation between input bit-streams. While some operations such as minimum and maximum value functions require highly correlated inputs, some other such as multiplication operation need uncorrelated or independent inputs for accurate computation. Developing low-cost and accurate correlation manipulation circuits is an important research in SC as these circuits can manage correlation between bit-streams without expensive bit-stream regeneration. This work proposes a novel in-stream correlator and decorrelator circuit that manages 1) correlation between stochastic bit-streams, and 2) distribution of 1’s in the output bit-streams. Compared to state-of-the-art solutions, our designs achieve lower hardware cost and higher accuracy. The output bit-streams enjoy a low-discrepancy distribution of bits which leads to higher quality of results. The effectiveness of the proposed circuits is shown with two case studies: SC design of sorting and median filtering 

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.