# Learning from Hypervectors: A Survey on Hypervector Encoding

arXiv: https://arxiv.org/abs/2308.00685 · HTML: https://mlsanity.com/paper/2308.00685/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 49 of its 236 references that are in the map.
- First posted on arXiv 1 Aug 2023.

## Builds on (most cited first, 30 of 49)

- [A Survey on Hyperdimensional Computing aka Vector Symbolic Architectures, Part I: Models and Data Transformations](https://mlsanity.com/paper/2111.06077/) (2021)
- [Constrained Few-shot Class-incremental Learning](https://mlsanity.com/paper/2203.16588/) (2022)
- [A Survey on Hyperdimensional Computing aka Vector Symbolic Architectures, Part II: Applications, Cognitive Models, and Challenges](https://mlsanity.com/paper/2112.15424/) (2021)
- [A comparison of Vector Symbolic Architectures](https://mlsanity.com/paper/2001.11797/) (2020)
- [Classification using Hyperdimensional Computing: A Review](https://mlsanity.com/paper/2004.11204/) (2020)
- [In-memory hyperdimensional computing](https://mlsanity.com/paper/1906.01548/) (2019)
- [A Theoretical Perspective on Hyperdimensional Computing](https://mlsanity.com/paper/2010.07426/) (2020)
- [Vector Symbolic Architectures as a Computing Framework for Emerging Hardware](https://mlsanity.com/paper/2106.05268/) (2021)
- [Robust High-dimensional Memory-augmented Neural Networks](https://mlsanity.com/paper/2010.01939/) (2020)
- [Random-projection ensemble classification](https://mlsanity.com/paper/1504.04595/) (2015)
- [Understanding Hyperdimensional Computing for Parallel Single-Pass Learning](https://mlsanity.com/paper/2202.04805/) (2022)
- [LeHDC: Learning-Based Hyperdimensional Computing Classifier](https://mlsanity.com/paper/2203.09680/) (2022)
- [Deep Triplet Quantization](https://mlsanity.com/paper/1902.00153/) (2019)
- [Hypervector Design for Efficient Hyperdimensional Computing on Edge Devices](https://mlsanity.com/paper/2103.06709/) (2021)
- [Hardware Optimizations of Dense Binary Hyperdimensional Computing: Rematerialization of Hypervectors, Binarized Bundling, and Combinational Associative Memory](https://mlsanity.com/paper/1807.08583/) (2018)
- [One-shot Learning for iEEG Seizure Detection Using End-to-end Binary Operations: Local Binary Patterns with Hyperdimensional Computing](https://mlsanity.com/paper/1809.01926/) (2018)
- [An EMG Gesture Recognition System with Flexible High-Density Sensors and Brain-Inspired High-Dimensional Classifier](https://mlsanity.com/paper/1802.10237/) (2018)
- [DistHD: A Learner-Aware Dynamic Encoding Method for Hyperdimensional Classification](https://mlsanity.com/paper/2304.05503/) (2023)
- [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)
- [Holographic Graph Neuron: a Bio-Inspired Architecture for Pattern Processing](https://mlsanity.com/paper/1501.03784/) (2015)
- [In-memory factorization of holographic perceptual representations](https://mlsanity.com/paper/2211.05052/) (2022)
- [In-memory Realization of In-situ Few-shot Continual Learning with a Dynamically Evolving Explicit Memory](https://mlsanity.com/paper/2207.06810/) (2022)
- [Prive-HD: Privacy-Preserved Hyperdimensional Computing](https://mlsanity.com/paper/2005.06716/) (2020)
- [Hyperdimensional Computing for Efficient Distributed Classification with Randomized Neural Networks](https://mlsanity.com/paper/2106.00881/) (2021)
- [MIMHD: Accurate and Efficient Hyperdimensional Inference Using Multi-Bit In-Memory Computing](https://mlsanity.com/paper/2106.12029/) (2021)
- [Exploring Embedding Methods in Binary Hyperdimensional Computing: A Case Study for Motor-Imagery based Brain-Computer Interfaces](https://mlsanity.com/paper/1812.05705/) (2018)
- [HDTorch: Accelerating Hyperdimensional Computing with GP-GPUs for Design Space Exploration](https://mlsanity.com/paper/2206.04746/) (2022)
- [Task-Projected Hyperdimensional Computing for Multi-Task Learning](https://mlsanity.com/paper/2004.14252/) (2020)
- [SynergicLearning: Neural Network-Based Feature Extraction for Highly-Accurate Hyperdimensional Learning](https://mlsanity.com/paper/2007.15222/) (2020)

## Built on by (most cited first, 7 of 7)

- [Exploring Effects of Hyperdimensional Vectors for Tsetlin Machines](https://mlsanity.com/paper/2406.02648/) (2024)
- [Sobol Sequence Optimization for Hardware-Efficient Vector Symbolic Architectures](https://mlsanity.com/paper/2311.10277/) (2023)
- [HEAL: Brain-inspired Hyperdimensional Efficient Active Learning](https://mlsanity.com/paper/2402.11223/) (2024)
- [HD-CB: The First Exploration of Hyperdimensional Computing for Contextual Bandits Problems](https://mlsanity.com/paper/2501.16863/) (2025)
- [uHD: Unary Processing for Lightweight and Dynamic Hyperdimensional Computing](https://mlsanity.com/paper/2311.10778/) (2023)
- [Deep-Learned Compression for Radio-Frequency Signal Classification](https://mlsanity.com/paper/2403.03150/) (2024)
- [CiliaGraph: Enabling Expression-enhanced Hyper-Dimensional Computation in Ultra-Lightweight and One-Shot Graph Classification on Edge](https://mlsanity.com/paper/2405.19033/) (2024)

## Abstract (arXiv)

Hyperdimensional computing (HDC) is an emerging computing paradigm that imitates the brain's structure to offer a powerful and efficient processing and learning model. In HDC, the data are encoded with long vectors, called hypervectors, typically with a length of 1K to 10K. The literature provides several encoding techniques to generate orthogonal or correlated hypervectors, depending on the intended application. The existing surveys in the literature often focus on the overall aspects of HDC systems, including system inputs, primary computations, and final outputs. However, this study takes a more specific approach. It zeroes in on the HDC system input and the generation of hypervectors, directly influencing the hypervector encoding process. This survey brings together various methods for hypervector generation from different studies and explores the limitations, challenges, and potential benefits they entail. Through a comprehensive exploration of this survey, readers will acquire a profound understanding of various encoding types in HDC and gain insights into the intricate process of hypervector generation for diverse applications.