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Hyperdimensional computing (HDC) is a novel computational paradigm thatoperates on long-dimensional vectors known as hypervectors. The hypervectorsare constructed as long bit-streams and form the basic building blocks of HDCsystems. In HDC, hypervectors are generated from scalar values without takingtheir bit significance into consideration. HDC has been shown to be efficientand robust in various data processing applications, including computer visiontasks. To construct HDC models for vision applications, the currentstate-of-the-art practice utilizes two parameters for data encoding: pixelintensity and pixel position. However, the intensity and position informationembedded in high-dimensional vectors are generally not generated dynamically inthe HDC models. Consequently, the optimal design of hypervectors with highmodel accuracy requires powerful computing platforms for training. A moreefficient approach to generating hypervectors is to create them dynamicallyduring the training phase, which results in accurate, low-cost, and highlyperformable vectors. To this aim, we use low-discrepancy sequences to generateintensity hypervectors only, while avoiding position hypervectors. By doing so,the multiplication step in vector encoding is eliminated, resulting in apower-efficient HDC system. For the first time in the literature, our proposedapproach employs lightweight vector generators utilizing unary bit-streams forefficient encoding of data instead of using conventional comparator-basedgenerators.

uHD: Unary Processing for Lightweight and Dynamic Hyperdimensional Computing