# Torchhd: An Open Source Python Library to Support Research on Hyperdimensional Computing and Vector Symbolic Architectures

arXiv: https://arxiv.org/abs/2205.09208 · HTML: https://mlsanity.com/paper/2205.09208/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 64 references that are in the map.
- First posted on arXiv 18 May 2022.

## Builds on (most cited first, 23 of 23)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Hopfield Networks is All You Need](https://mlsanity.com/paper/2008.02217/) (2020)
- [Dense Associative Memory for Pattern Recognition](https://mlsanity.com/paper/1606.01164/) (2016)
- [On the Binding Problem in Artificial Neural Networks](https://mlsanity.com/paper/2012.05208/) (2020)
- [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)
- [A Neuro-vector-symbolic Architecture for Solving Raven's Progressive Matrices](https://mlsanity.com/paper/2203.04571/) (2022)
- [Classification using Hyperdimensional Computing: A Review](https://mlsanity.com/paper/2004.11204/) (2020)
- [Neurocompositional computing: From the Central Paradox of Cognition to a new generation of AI systems](https://mlsanity.com/paper/2205.01128/) (2022)
- [Computing on Functions Using Randomized Vector Representations](https://mlsanity.com/paper/2109.03429/) (2021)
- [Vector Symbolic Architectures as a Computing Framework for Emerging Hardware](https://mlsanity.com/paper/2106.05268/) (2021)
- [A theory of sequence indexing and working memory in recurrent neural networks](https://mlsanity.com/paper/1803.00412/) (2018)
- [GraphHD: Efficient graph classification using hyperdimensional computing](https://mlsanity.com/paper/2205.07826/) (2022)
- [Variable Binding for Sparse Distributed Representations: Theory and Applications](https://mlsanity.com/paper/2009.06734/) (2020)
- [Learning with Holographic Reduced Representations](https://mlsanity.com/paper/2109.02157/) (2021)
- [Density Encoding Enables Resource-Efficient Randomly Connected Neural Networks](https://mlsanity.com/paper/1909.09153/) (2019)
- [Generalized Learning Vector Quantization for Classification in Randomized Neural Networks and Hyperdimensional Computing](https://mlsanity.com/paper/2106.09821/) (2021)
- [Integer Factorization with Compositional Distributed Representations](https://mlsanity.com/paper/2203.00920/) (2022)
- [An Extension to Basis-Hypervectors for Learning from Circular Data in Hyperdimensional Computing](https://mlsanity.com/paper/2205.07920/) (2022)
- [Hyperdimensional Hashing: A Robust and Efficient Dynamic Hash Table](https://mlsanity.com/paper/2205.07850/) (2022)

## Built on by (most cited first, 17 of 17)

- [A Walsh Hadamard Derived Linear Vector Symbolic Architecture](https://mlsanity.com/paper/2410.22669/) (2024)
- [HEAL: Brain-inspired Hyperdimensional Efficient Active Learning](https://mlsanity.com/paper/2402.11223/) (2024)
- [MicroHD: An Accuracy-Driven Optimization of Hyperdimensional Computing Algorithms for TinyML systems](https://mlsanity.com/paper/2404.00039/) (2024)
- [Hyperdimensional Probe: Decoding LLM Representations via Vector Symbolic Architectures](https://mlsanity.com/paper/2509.25045/) (2025)
- [Hey Pentti, We Did It!: A Fully Vector-Symbolic Lisp](https://mlsanity.com/paper/2510.17889/) (2025)
- [Molecular Classification Using Hyperdimensional Graph Classification](https://mlsanity.com/paper/2403.12307/) (2024)
- [Enhanced Detection of Transdermal Alcohol Levels Using Hyperdimensional Computing on Embedded Devices](https://mlsanity.com/paper/2403.12323/) (2024)
- [DEBUG-HD: Debugging TinyML models on-device using Hyper-Dimensional computing](https://mlsanity.com/paper/2411.10692/) (2024)
- [HYPERDOA: Robust and Efficient DoA Estimation using Hyperdimensional Computing](https://mlsanity.com/paper/2510.10718/) (2025)
- [CiliaGraph: Enabling Expression-enhanced Hyper-Dimensional Computation in Ultra-Lightweight and One-Shot Graph Classification on Edge](https://mlsanity.com/paper/2405.19033/) (2024)
- [Privacy-Preserving Federated Learning with Differentially Private Hyperdimensional Computing](https://mlsanity.com/paper/2411.01140/) (2024)
- [ScalableHD: Scalable and High-Throughput Hyperdimensional Computing Inference on Multi-Core CPUs](https://mlsanity.com/paper/2506.09282/) (2025)
- [BiHDTrans: binary hyperdimensional transformer for efficient multivariate time series classification](https://mlsanity.com/paper/2509.24425/) (2025)
- [hdlib 2.0: Extending Machine Learning Capabilities of Vector-Symbolic Architectures](https://mlsanity.com/paper/2601.02509/) (2026)
- [HyperSpace: A Generalized Framework for Spatial Encoding in Hyperdimensional Representations](https://mlsanity.com/paper/2604.15113/) (2026)
- [QeHDC: Hyperdimensional Computing based on Quantum-enhanced binding and SuperClass Construction](https://mlsanity.com/paper/2606.22421/) (2026)
- [Compiler-Driven Approximation Tuning for Hyperdimensional Computing](https://mlsanity.com/paper/2606.26547/) (2026)

## Abstract (arXiv)

Hyperdimensional computing (HD), also known as vector symbolic architectures (VSA), is a framework for computing with distributed representations by exploiting properties of random high-dimensional vector spaces. The commitment of the scientific community to aggregate and disseminate research in this particularly multidisciplinary area has been fundamental for its advancement. Joining these efforts, we present Torchhd, a high-performance open source Python library for HD/VSA. Torchhd seeks to make HD/VSA more accessible and serves as an efficient foundation for further research and application development. The easy-to-use library builds on top of PyTorch and features state-of-the-art HD/VSA functionality, clear documentation, and implementation examples from well-known publications. Comparing publicly available code with their corresponding Torchhd implementation shows that experiments can run up to 100x faster. Torchhd is available at: https://github.com/hyperdimensional-computing/torchhd.