# Streaming Encoding Algorithms for Scalable Hyperdimensional Computing

arXiv: https://arxiv.org/abs/2209.09868 · HTML: https://mlsanity.com/paper/2209.09868/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 76 references that are in the map.
- First posted on arXiv 20 Sep 2022.

## Builds on (most cited first, 17 of 17)

- [Convex optimization](https://mlsanity.com/paper/2106.01946/) (2021)
- [Compressing Neural Networks with the Hashing Trick](https://mlsanity.com/paper/1504.04788/) (2015)
- [A Survey on Hyperdimensional Computing aka Vector Symbolic Architectures, Part I: Models and Data Transformations](https://mlsanity.com/paper/2111.06077/) (2021)
- [A Survey on Hyperdimensional Computing aka Vector Symbolic Architectures, Part II: Applications, Cognitive Models, and Challenges](https://mlsanity.com/paper/2112.15424/) (2021)
- [Classification using Hyperdimensional Computing: A Review](https://mlsanity.com/paper/2004.11204/) (2020)
- [MLlib: Machine Learning in Apache Spark](https://mlsanity.com/paper/1505.06807/) (2015)
- [In-memory hyperdimensional computing](https://mlsanity.com/paper/1906.01548/) (2019)
- [A Theoretical Perspective on Hyperdimensional Computing](https://mlsanity.com/paper/2010.07426/) (2020)
- [A theory of sequence indexing and working memory in recurrent neural networks](https://mlsanity.com/paper/1803.00412/) (2018)
- [Variable Binding for Sparse Distributed Representations: Theory and Applications](https://mlsanity.com/paper/2009.06734/) (2020)
- [Hardware Optimizations of Dense Binary Hyperdimensional Computing: Rematerialization of Hypervectors, Binarized Bundling, and Combinational Associative Memory](https://mlsanity.com/paper/1807.08583/) (2018)
- [Autoscaling Bloom Filter: Controlling Trade-off Between True and False Positives](https://mlsanity.com/paper/1705.03934/) (2017)
- [Generalized Learning Vector Quantization for Classification in Randomized Neural Networks and Hyperdimensional Computing](https://mlsanity.com/paper/2106.09821/) (2021)
- [HyperEmbed: Tradeoffs Between Resources and Performance in NLP Tasks with Hyperdimensional Computing enabled Embedding of n-gram Statistics](https://mlsanity.com/paper/2003.01821/) (2020)
- [Semantically Constrained Memory Allocation (SCMA) for Embedding in Efficient Recommendation Systems](https://mlsanity.com/paper/2103.06124/) (2021)
- [MIMHD: Accurate and Efficient Hyperdimensional Inference Using Multi-Bit In-Memory Computing](https://mlsanity.com/paper/2106.12029/) (2021)
- [Expressivity of expand-and-sparsify representations](https://mlsanity.com/paper/2006.03741/) (2020)

## Built on by (most cited first, 3 of 3)

- [Learning from Hypervectors: A Survey on Hypervector Encoding](https://mlsanity.com/paper/2308.00685/) (2023)
- [Capacity Analysis of Vector Symbolic Architectures](https://mlsanity.com/paper/2301.10352/) (2023)
- [On Design Choices in Similarity-Preserving Sparse Randomized Embeddings](https://mlsanity.com/paper/2501.14741/) (2024)

## Abstract (arXiv)

Hyperdimensional computing (HDC) is a paradigm for data representation and learning originating in computational neuroscience. HDC represents data as high-dimensional, low-precision vectors which can be used for a variety of information processing tasks like learning or recall. The mapping to high-dimensional space is a fundamental problem in HDC, and existing methods encounter scalability issues when the input data itself is high-dimensional. In this work, we explore a family of streaming encoding techniques based on hashing. We show formally that these methods enjoy comparable guarantees on performance for learning applications while being substantially more efficient than existing alternatives. We validate these results experimentally on a popular high-dimensional classification problem and show that our approach easily scales to very large data sets.