HDC-X: Efficient Medical Data Classification for Embedded Devices

• URL: http://arxiv.org/abs/2509.14617v2
• Date: Sun, 21 Sep 2025 15:09:06 GMT
• Title: HDC-X: Efficient Medical Data Classification for Embedded Devices
• Authors: Jianglan Wei, Zhenyu Zhang, Pengcheng Wang, Mingjie Zeng, Zhigang Zeng,
• Abstract summary: HDC-X is a lightweight classification framework designed for low-power devices.<n>On heart sound classification, HDC-X is $350times$ more energy-efficient than Bayesian ResNet with less than 1% accuracy difference.
• Score: 44.602566101469726
• License: http://creativecommons.org/licenses/by/4.0/
• 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 is available at https://github.com/jianglanwei/HDC-X.

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