# HDCC: A Hyperdimensional Computing compiler for classification on embedded systems and high-performance computing

arXiv: https://arxiv.org/abs/2304.12398 · HTML: https://mlsanity.com/paper/2304.12398/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 32 references that are in the map.
- First posted on arXiv 24 Apr 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)
- [A comparison of Vector Symbolic Architectures](https://mlsanity.com/paper/2001.11797/) (2020)
- [GraphHD: Efficient graph classification using hyperdimensional computing](https://mlsanity.com/paper/2205.07826/) (2022)
- [HDTorch: Accelerating Hyperdimensional Computing with GP-GPUs for Design Space Exploration](https://mlsanity.com/paper/2206.04746/) (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, 3 of 3)

- [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)
- [ScalableHD: Scalable and High-Throughput Hyperdimensional Computing Inference on Multi-Core CPUs](https://mlsanity.com/paper/2506.09282/) (2025)

## Abstract (arXiv)

Hyperdimensional Computing (HDC) is a bio-inspired computing framework that has gained increasing attention, especially as a more efficient approach to machine learning (ML). This work introduces the name compiler, the first open-source compiler that translates high-level descriptions of HDC classification methods into optimized C code. The code generated by the proposed compiler has three main features for embedded systems and High-Performance Computing: (1) it is self-contained and has no library or platform dependencies; (2) it supports multithreading and single instruction multiple data (SIMD) instructions using C intrinsics; (3) it is optimized for maximum performance and minimal memory usage. name is designed like a modern compiler, featuring an intuitive and descriptive input language, an intermediate representation (IR), and a retargetable backend. This makes name a valuable tool for research and applications exploring HDC for classification tasks on embedded systems and High-Performance Computing. To substantiate these claims, we conducted experiments with HDCC on several of the most popular datasets in the HDC literature. The experiments were run on four different machines, including different hyperparameter configurations, and the results were compared to a popular prototyping library built on PyTorch. The results show a training and inference speedup of up to 132x, averaging 25x across all datasets and machines. Regarding memory usage, using 10240-dimensional hypervectors, the average reduction was 5x, reaching up to 14x. When considering vectors of 64 dimensions, the average reduction was 85x, with a maximum of 158x less memory utilization.