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

• URL: http://arxiv.org/abs/2507.17953v1
• Date: Wed, 23 Jul 2025 21:50:28 GMT
• 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
• 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,
• Abstract summary: Clo-HDnn is an on-device learning (ODL) accelerator designed for emerging continual learning (CL) tasks.<n>Its dual-mode operation enables bypassing costly feature extraction for simpler datasets.<n>Its progressive search reduces complexity by up to 61% by encoding and comparing only partial query hypervectors.
• Score: 7.700041585751539
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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