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.

翻译：超维计算（HDC）是一种新型计算范式，其操作对象为称为超向量的长维度向量。超向量以长比特流形式构建，构成HDC系统的基本模块。在HDC中，超向量由标量值生成，而不考虑其比特重要性。已有研究表明，HDC在包括计算机视觉任务在内的多种数据处理应用中具有高效性和鲁棒性。为构建面向视觉应用的HDC模型，当前最先进的实践采用像素强度和像素位置两个参数进行数据编码。然而，嵌入在高维向量中的强度与位置信息在HDC模型中通常并非动态生成。因此，设计具有高模型准确率的超向量需要强大的计算平台进行训练。一种更高效的超向量生成方法是在训练阶段动态构建，从而获得准确、低成本且高性能的向量。为此，我们使用低差异序列仅生成强度超向量，避免使用位置超向量。通过这种方式，向量编码中的乘法步骤被消除，从而构建出高能效的HDC系统。据文献记载，本方法首次采用基于单值比特流的轻量级向量生成器实现高效数据编码，而非传统基于比较器的生成器方案。