LeHDC: Learning-Based Hyperdimensional Computing Classifier

• URL: http://arxiv.org/abs/2203.09680v1
• Date: Fri, 18 Mar 2022 01:13:58 GMT
• Title: LeHDC: Learning-Based Hyperdimensional Computing Classifier
• Authors: Shijin Duan, Yejia Liu, Shaolei Ren, and Xiaolin Xu
• Abstract summary: We propose a new HDC framework, called LeHDC, which leverages a principled learning approach to improve the model accuracy. Experimental validation shows that LeHDC outperforms previous HDC training strategies and can improve on average the inference accuracy over 15%.
• Score: 14.641707790969914
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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