AN-YEU(ANDY) WU2022-05-192022-05-1920219.78303E+1218684238https://www.scopus.com/inward/record.uri?eid=2-s2.0-85111875823&doi=10.1007%2f978-3-030-79150-6_35&partnerID=40&md5=9b28392cc79ff159647c71615f842ddehttps://scholars.lib.ntu.edu.tw/handle/123456789/611199Brain-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. © 2021, IFIP International Federation for Information Processing.Brain-inspired computing; Hyperdimensional computing; User adaptation[SDGs]SDG7Associative processing; Energy efficiency; Green computing; Turing machines; Accuracy Improvement; Associative memory; Benchmark datasets; Fusion mechanism; High energy efficiency; Real-world task; Resource-limited devices; User adaptation; Artificial intelligenceconference paper10.1007/978-3-030-79150-6_352-s2.0-85111875823