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Abstract—Hyperdimensional Computing (HDC) is a neurallyinspired computation model working based on the observation that the human brain operates on high-dimensional representations of data, called hypervector. Although HDC is significantly powerful in reasoning and association of the abstract information, it is weak on features extraction from complex data such as image/video. As a result, most existing HDC solutions rely on expensive pre-processing algorithms for feature extraction. In this paper, we propose StocHD, a novel end-to-end hyperdimensional system that supports accurate, efficient, and robust learning over raw data. Unlike prior work that used HDC for learning tasks, StocHD expands HDC functionality to the computing area by mathematically defining stochastic arithmetic over HDC hypervectors. StocHD enables an entire learning application (including feature extractor) to process using HDC data representation, enabling uniform, efficient, robust, and highly parallel computation. We also propose a novel fully digital and scalable Processing In-Memory (PIM) architecture that exploits the HDC memorycentric nature to support extensively parallel computation. Our evaluation over a wide range of classification tasks shows that StocHD provides, on average, 3.3x and 6.4x (52.3x and 143.Sx) faster and higher energy efficiency as compared to state-of-the-art HDC algorithm running on PIM (NVIDIA GPU), while providing 16x higher computational robustness.
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Lipschitz-based robustness estimation for hyperdimensional learning
With the adoption of machine learning models in various practical domains, there is a growing need for evaluating and increasing model robustness. Hyperdimensional computing (HDC) is a neurosymbolic computational paradigm that represents symbols as high dimensional vectors and symbolic operations as vector operations, seamlessly interfacing between neuro- and symbolic components of a model. However, there is a notable gap in HDC research regarding the robustness of HDC models to input perturbations. This study presents a novel theoretical framework tailored to evaluate the robustness of hyperdimensional classifiers against perturbations in the input space. In particular, our proposed measure of robustness gives a theoretical upper bound for the magnitude of noise a model can tolerate without changing its prediction for any given data point. We also propose a method to enhance the robustness of the model based on our proposed measure of robustness. Our approach introduces several methods to calculate model robustness as a function of the specific dataset and type of hyperdimensional encoding used. The results show that the average robustness of HDC models increases under the proposed optimization scheme while maintaining accuracy by varying the variance of the Gaussian distribution used to encode hypervectors. The practical effectiveness of our proposed measure of robustness is also demonstrated.
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- PAR ID:
- 10688708
- Publisher / Repository:
- Frontiers in Artificial Intelligence
- Date Published:
- Journal Name:
- Frontiers in Artificial Intelligence
- Volume:
- 8
- ISSN:
- 2624-8212
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript
- Journal Article:
- https://doi.org/10.3389/frai.2025.1637105
