LeHDC: Learning-Based Hyperdimensional Computing Classifier

2022/03/18 by Shijin Duan, Yejia Liu, Duan, Shijin +5 · 2 citations
Engineering · Physics and Astronomy · Computer Science · #Ferroelectric and Negative Capacitance Devices #Magnetic properties of thin films #Parallel Computing and Optimization Techniques

paper · pdf · doi:10.48550/arxiv.2203.09680

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.

Cited by

• Quantum Hyperdimensional Computing: a foundational paradigm for quantum neuromorphic architectures
• BiHDTrans: binary hyperdimensional transformer for efficient multivariate time series classification

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