The intersection of computer engineering and medical sciences represents one of the most transformative technical domains of our era. Edge Artificial Intelligence (Edge AI) has emerged as a paramount architectural paradigm for modern medical applications. Unlike traditional cloud-centric computing paradigms, Edge AI processes biomedical data locally on embedded wearable devices or diagnostic equipment. This approach ensures ultra-low latency, optimizes network bandwidth utilization, and significantly enhances patient data privacy and security. 2. Architectural Framework & Hardware Accelerators Executing complex deep learning models and neural networks on resource-constrained embedded systems necessitates specialized hardware architecture designed for high throughput and extreme energy efficiency. Key hardware accelerators include: • Neural Processing Units (NPUs): Custom-designed silicon cores engineered specifically to accelerate mathematical operations inherent in deep neural networks, such as matrix multiplication and convolution. • Field-Programmable Gate Arrays (FPGAs): Offer hardware-level reconfigurability, allowing engineers to tailor processing pipelines for specific diagnostic tasks like real-time ECG anomaly detection. • Application-Specific Integrated Circuits (ASICs): Provide unmatched energy efficiency and processing performance tailored for continuous monitoring medical implants and portable gear.