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HD3C introduces a novel hypervector-based framework that significantly improves energy efficiency for medical data classification on resource-constrained edge devices. By leveraging high-dimensional encoding and similarity search, it achieves near-parity accuracy with standard models while offering robustness against noise and hardware errors. This approach addresses critical deployment challenges in low-power environments without relying solely on increased computational throughput.

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HD3C: Efficient Medical Data Classification for Edge Devices

arXiv27 iulie 20262 min de cititArticolul și perspectiva sunt în engleză.

arXiv:2509.14617v4 Announce Type: replace Abstract: Efficient medical data classification is essential for modern disease screening, particularly in resource-constrained environments where power budgets and computing capabilities are limited. We present HD3C, a lightweight classification framework designed for low-power edge devices. HD3C encodes data into high-dimensional hypervectors, aggregates them into multiple cluster prototypes, and performs classification through similarity search in hyperspace. We evaluate HD3C across three medical classification tasks; on heart sound classification, HD3C is 350x more energy-efficient than Bayesian ResNet with less than 1% difference in accuracy. Moreover, HD3C 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.

Abstract:Efficient medical data classification is essential for modern disease screening, particularly in resource-constrained environments where power budgets and computing capabilities are limited. We present HD3C, a lightweight classification framework designed for low-power edge devices. HD3C encodes data into high-dimensional hypervectors, aggregates them into multiple cluster prototypes, and performs classification through similarity search in hyperspace. We evaluate HD3C across three medical classification tasks; on heart sound classification, HD3C is 350x more energy-efficient than Bayesian ResNet with less than 1% difference in accuracy. Moreover, HD3C 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.

Machine Learning (cs.LG)

arXiv:2509.14617 [cs.LG]

(or arXiv:2509.14617v4 [cs.LG] for this version)

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