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Conference Papers Year : 2021

Hyperdimensional Computing with Learnable Projection for User Adaptation Framework

Yu-Ren Hsiao
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  • PersonId : 1105455
Yu-Chuan Chuang
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  • PersonId : 1105414
Cheng-Yang Chang
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  • PersonId : 1105413
An-Yeu (andy) Wu
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  • PersonId : 1105411

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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hal-03287688 , version 1 (15-07-2021)

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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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