[2509.14617] HDC-X: Efficient Medical Data Classification for Embedded Devices

Computer Science > Machine Learning arXiv:2509.14617 (cs) [Submitted on 18 Sep 2025 (v1), last revised 23 Mar 2026 (this version, v3)] Title:HDC-X: Efficient Medical Data Classification for Embedded Devices Authors:Jianglan Wei, Zhenyu Zhang, Pengcheng Wang, Mingjie Zeng, Zhigang Zeng View a PDF of the paper titled HDC-X: Efficient Medical Data Classification for Embedded Devices, by Jianglan Wei and 4 other authors View PDF HTML (experimental) Abstract:Energy-efficient medical data classification is essential for modern disease screening, particularly in home and field healthcare where embedded devices are prevalent. While deep learning models achieve state-of-the-art accuracy, their substantial energy consumption and reliance on GPUs limit deployment on such platforms. We present HDC-X, a lightweight classification framework designed for low-power devices. HDC-X encodes data into high-dimensional hypervectors, aggregates them into multiple cluster-specific prototypes, and performs classification through similarity search in hyperspace. We evaluate HDC-X across three medical classification tasks; on heart sound classification, HDC-X is $350\times$ more energy-efficient than Bayesian ResNet with less than 1% accuracy difference. Moreover, HDC-X demonstrates exceptional robustness to noise, limited training data, and hardware error, supported by both theoretical analysis and empirical results, highlighting its potential for reliable deployment in real-world settings. Code i...