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.

翻译：节能型医疗数据分类对于现代疾病筛查至关重要，尤其是在嵌入式设备普及的家庭和现场医疗场景中。尽管深度学习模型达到了最先进的准确率，但其高能耗和对GPU的依赖限制了在此类平台上的部署。我们提出HDC-X，一种面向低功耗设备的轻量级分类框架。HDC-X将数据编码为高维超向量，并将其聚合为多个聚类特异性原型，通过超空间中的相似性搜索完成分类。我们在三项医疗分类任务上评估了HDC-X；在心音分类任务中，HDC-X的能效比贝叶斯残差网络（Bayesian ResNet）高出350倍，而准确率差异小于1%。此外，理论分析与实证结果均表明，HDC-X对噪声、有限训练数据和硬件误差具有卓越的鲁棒性，凸显了其在真实场景中可靠部署的潜力。代码已开源：https://github.com/jianglanwei/HDC-X。