  Hyperdimensional computing (HDC) is a novel computational paradigm that
operates on long-dimensional vectors known as hypervectors. The hypervectors
are constructed as long bit-streams and form the basic building blocks of HDC
systems. In HDC, hypervectors are generated from scalar values without taking
their bit significance into consideration. HDC has been shown to be efficient
and robust in various data processing applications, including computer vision
tasks. To construct HDC models for vision applications, the current
state-of-the-art practice utilizes two parameters for data encoding: pixel
intensity and pixel position. However, the intensity and position information
embedded in high-dimensional vectors are generally not generated dynamically in
the HDC models. Consequently, the optimal design of hypervectors with high
model accuracy requires powerful computing platforms for training. A more
efficient approach to generating hypervectors is to create them dynamically
during the training phase, which results in accurate, low-cost, and highly
performable vectors. To this aim, we use low-discrepancy sequences to generate
intensity hypervectors only, while avoiding position hypervectors. By doing so,
the multiplication step in vector encoding is eliminated, resulting in a
power-efficient HDC system. For the first time in the literature, our proposed
approach employs lightweight vector generators utilizing unary bit-streams for
efficient encoding of data instead of using conventional comparator-based
generators.
