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arXiv · 2203.09680

LeHDC: Learning-Based Hyperdimensional Computing Classifier

Shijin Duan·Yejia Liu·Shaolei Ren·Xiaolin Xu
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Abstract

Thanks to the tiny storage and efficient execution, hyperdimensional Computing (HDC) is emerging as a lightweight learning framework on resource-constrained hardware. Nonetheless, the existing HDC training relies on various heuristic methods, significantly limiting their inference accuracy. In this paper, we propose a new HDC framework, called LeHDC, which leverages a principled learning approach to improve the model accuracy. Concretely, LeHDC maps the existing HDC framework into an equivalent Binary Neural Network architecture, and employs a corresponding training strategy to minimize the training loss. Experimental validation shows that LeHDC outperforms previous HDC training strategies and can improve on average the inference accuracy over 15% compared to the baseline HDC.

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Shijin Duan, Yejia Liu, Shaolei Ren, Xiaolin Xu. 2022-03-18. LeHDC: Learning-Based Hyperdimensional Computing Classifier. https://arxiv.org/abs/2203.09680

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