{"ID":2891536,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.17953","arxiv_id":"2507.17953","title":"Clo-HDnn: A 4.66 TFLOPS/W and 3.78 TOPS/W Continual On-Device Learning Accelerator with Energy-efficient Hyperdimensional Computing via Progressive Search","abstract":"Clo-HDnn is an on-device learning (ODL) accelerator designed for emerging continual learning (CL) tasks. Clo-HDnn integrates hyperdimensional computing (HDC) along with low-cost Kronecker HD Encoder and weight clustering feature extraction (WCFE) to optimize accuracy and efficiency. Clo-HDnn adopts gradient-free CL to efficiently update and store the learned knowledge in the form of class hypervectors. Its dual-mode operation enables bypassing costly feature extraction for simpler datasets, while progressive search reduces complexity by up to 61% by encoding and comparing only partial query hypervectors. Achieving 4.66 TFLOPS/W (FE) and 3.78 TOPS/W (classifier), Clo-HDnn delivers 7.77x and 4.85x higher energy efficiency compared to SOTA ODL accelerators.","short_abstract":"Clo-HDnn is an on-device learning (ODL) accelerator designed for emerging continual learning (CL) tasks. Clo-HDnn integrates hyperdimensional computing (HDC) along with low-cost Kronecker HD Encoder and weight clustering feature extraction (WCFE) to optimize accuracy and efficiency. Clo-HDnn adopts gradient-free CL to...","url_abs":"https://arxiv.org/abs/2507.17953","url_pdf":"https://arxiv.org/pdf/2507.17953v1","authors":"[\"Chang Eun Song\",\"Weihong Xu\",\"Keming Fan\",\"Soumil Jain\",\"Gopabandhu Hota\",\"Haichao Yang\",\"Leo Liu\",\"Kerem Akarvardar\",\"Meng-Fan Chang\",\"Carlos H. Diaz\",\"Gert Cauwenberghs\",\"Tajana Rosing\",\"Mingu Kang\"]","published":"2025-07-23T21:50:28Z","proceeding":"cs.AR","tasks":"[\"cs.AR\",\"cs.LG\"]","methods":"[]","has_code":false}