# QubitHD: A Stochastic Acceleration Method for HD Computing-Based Machine Learning

arXiv: https://arxiv.org/abs/1911.12446 · HTML: https://mlsanity.com/paper/1911.12446/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Nov 2019.

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

- [Learning from Hypervectors: A Survey on Hypervector Encoding](https://mlsanity.com/paper/2308.00685/) (2023)
- [An Encoding Framework for Binarized Images using HyperDimensional Computing](https://mlsanity.com/paper/2312.00454/) (2023)
- [A Mathematical Framework for the Problem of Security for Cognition in Neurotechnology](https://mlsanity.com/paper/2403.07945/) (2024)

## Abstract (arXiv)

Machine Learning algorithms based on Brain-inspired Hyperdimensional(HD) computing imitate cognition by exploiting statistical properties of high-dimensional vector spaces. It is a promising solution for achieving high energy efficiency in different machine learning tasks, such as classification, semi-supervised learning, and clustering. A weakness of existing HD computing-based ML algorithms is the fact that they have to be binarized to achieve very high energy efficiency. At the same time, binarized models reach lower classification accuracies. To solve the problem of the trade-off between energy efficiency and classification accuracy, we propose the QubitHD algorithm. It stochastically binarizes HD-based algorithms, while maintaining comparable classification accuracies to their non-binarized counterparts. The FPGA implementation of QubitHD provides a 65% improvement in terms of energy efficiency, and a 95% improvement in terms of training time, as compared to state-of-the-art HD-based ML algorithms. It also outperforms state-of-the-art low-cost classifiers (such as Binarized Neural Networks) in terms of speed and energy efficiency by an order of magnitude during training and inference.