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Hyperdimensional Computing with Learnable Projection for User Adaptation Framework

Abstract : Brain-inspired Hyperdimensional Computing (HDC), a machine learning (ML) model featuring high energy efficiency and fast adaptability, provides a promising solution to many real-world tasks on resource-limited devices. This paper introduces an HDC-based user adaptation framework, which requires efficient fine-tuning of HDC models to boost accuracy. Specifically, we propose two techniques for HDC, including the learnable projection and the fusion mechanism for the Associative Memory (AM). Compared with the user adaptation framework based on the original HDC, our proposed framework shows 4.8% and 3.5% of accuracy improvements on two benchmark datasets, including the ISOLET dataset and the UCIHAR dataset, respectively.
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https://hal.inria.fr/hal-03287688
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Submitted on : Thursday, July 15, 2021 - 6:11:09 PM
Last modification on : Friday, August 13, 2021 - 4:29:53 PM
Long-term archiving on: : Saturday, October 16, 2021 - 7:07:31 PM

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yu-Ren Hsiao, yu-Chuan Chuang, Cheng-yang Chang, An-yeu (andy) Wu. Hyperdimensional Computing with Learnable Projection for User Adaptation Framework. 17th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), Jun 2021, Hersonissos, Crete, Greece. pp.436-447, ⟨10.1007/978-3-030-79150-6_35⟩. ⟨hal-03287688⟩

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