Abstract
The demand for real-time biomedical signal processing has driven the evolution of Very Large Scale Integration (VLSI) architectures optimized for low power, high throughput, and miniaturized systems. From electrocardiogram (ECG) monitoring to brain-computer interfaces (BCIs), real-time processing ensures timely clinical decision-making and wearable device performance. This paper explores scalable VLSI architectures tailored for biomedical applications, focusing on pipelined processing units, noise-robust filters, and adaptive signal analytics. The integration of energy-efficient hardware components with domain-specific signal processors enhances functionality while minimizing area and power consumption. The study also discusses design trade-offs and highlights future directions in AI-assisted hardware acceleration for biomedical signals
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