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Hyperdimensional computing (HDC) is an emerging computing paradigm with significant promise for efficient and robust learning. In HDC, objects are encoded with high-dimensional vector symbolic sequences called hypervectors. The quality of hypervectors, defined by their distribution and independence, directly impacts the performance of HDC systems. Despite a large body of work on the processing parts of HDC systems, little to no attention has been paid to data encoding and the quality of hypervectors. Most prior studies have generated hypervectors using inherent random functions, such as MATLAB’s or Python’s random function. This work introduces an optimization technique for generating hypervectors by employing quasi-random sequences. These sequences have recently demonstrated their effectiveness in achieving accurate and low-discrepancy data encoding in stochastic computing systems. The study outlines the optimization steps for utilizing Sobol sequences to produce highquality hypervectors in HDC systems. An optimization algorithm is proposed to select the most suitable Sobol sequences via indexes for generating minimally correlated hypervectors, particularly in applications related to symbol-oriented architectures. The performance of the proposed technique is evaluated in comparison to two traditional approaches of generating hypervectors based on linear-feedback shift registers and MATLAB random functions. The evaluation is conducted for three applications: (i) language, (ii) headline, and (iii) medical image classification. Our experimental results demonstrate accuracy improvements of up to 10.79%, depending on the vector size. Additionally, the proposed encoding hardware exhibits reduced energy consumption and a superior area-delay product.
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This content will become publicly available on June 22, 2026
Late Breaking Results: On-the-Fly Hadamard Hypervector Processing for Efficient Hyperdimensional Computing
Inspired by the human brain, Hyperdimensional Computing (HDC) processes information efficiently by operating in high-dimensional space using hypervectors. While previous works focus on optimizing pre-generated hypervectors in software, this study introduces a novel on-the-fly vector generation method in hardware with O(1) complexity, compared to the O(N) iterative search used in conventional approaches to find the best orthogonal hypervectors. Our approach leverages Hadamard binary coefficients and unary computing to simplify encoding into addition-only operations after the generation stage in ASIC, implemented using in-memory computing. The proposed design significantly improves accuracy and computational efficiency across multiple benchmark datasets.
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- Award ID(s):
- 2339701
- PAR ID:
- 10656869
- Publisher / Repository:
- IEEE
- Date Published:
- Page Range / eLocation ID:
- 1 to 2
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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