High-Performance VLSI Architectures for Healthcare System using Machine Learning

Abstract

The increasing demand for intelligent, real-time healthcare monitoring and diagnosis has led to the integration of machine learning (ML) algorithms with high-performance VLSI architectures. This paper presents a novel VLSI design optimized for healthcare applications, offering high computational efficiency, low power consumption, and minimal latency. The proposed architecture leverages parallel processing elements, systolic array-based computation, and hierarchical memory organization to accelerate ML models such as XGBoost classifiers. These models are widely used in diagnosing diseases like diabetes, heart disorders, and arrhythmia. The design supports mixed-precision arithmetic and quantization-aware training to achieve optimal trade-offs between accuracy and energy efficiency. Moreover, the implementation incorporates sparsity exploitation, clock gating, and adaptive power management techniques to enhance performance for real-time physiological signal and medical image analysis. The architecture has been prototyped on FPGA and evaluated using standard healthcare datasets such as PhysioNet and MIMIC-III. Experimental results demonstrate significant improvements in throughput and power efficiency compared to traditional CPU/GPU-based implementations. This work establishes a scalable and reconfigurable VLSI platform capable of supporting diverse ML algorithms for healthcare diagnostics, enabling edge intelligence and ensuring data privacy within wearable and IoT-enabled medical devices.