# uHD: Unary Processing for Lightweight and Dynamic Hyperdimensional Computing

arXiv: https://arxiv.org/abs/2311.10778 · HTML: https://mlsanity.com/paper/2311.10778/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 36 references that are in the map.
- First posted on arXiv 16 Nov 2023.

## Builds on (most cited first, 6 of 6)

- [A Survey on Hyperdimensional Computing aka Vector Symbolic Architectures, Part I: Models and Data Transformations](https://mlsanity.com/paper/2111.06077/) (2021)
- [Classification using Hyperdimensional Computing: A Review](https://mlsanity.com/paper/2004.11204/) (2020)
- [Hypervector Design for Efficient Hyperdimensional Computing on Edge Devices](https://mlsanity.com/paper/2103.06709/) (2021)
- [PULP-HD: Accelerating Brain-Inspired High-Dimensional Computing on a Parallel Ultra-Low Power Platform](https://mlsanity.com/paper/1804.09123/) (2018)
- [A Brain-Inspired Low-Dimensional Computing Classifier for Inference on Tiny Devices](https://mlsanity.com/paper/2203.04894/) (2022)
- [Learning from Hypervectors: A Survey on Hypervector Encoding](https://mlsanity.com/paper/2308.00685/) (2023)

## Built on by (most cited first, 1 of 1)

- [AMS-HD: Hyperdimensional Computing for Real-Time and Energy-Efficient Acute Mountain Sickness Detection](https://mlsanity.com/paper/2602.08916/) (2026)

## Abstract (arXiv)

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.