Energy-efficient reconfigurable architectures for Edge AI in healthcare IoT: trends, challenges, and future directions

Abstract

The integration of Edge artificial intelligence (AI) with internet of things (IoT) technologies is transforming healthcare applications, including wearable monitoring, telemedicine, and implantable medical devices, by enabling low-latency and intelligent data processing close to patients. However, stringent requirements on energy efficiency, reliability, real-time responsiveness, and data privacy continue to hinder scalable and long-term deployment in resource-constrained healthcare environments. Energy-efficient reconfigurable architectures—such as field-programmable gate arrays (FPGAs), coarse-grained reconfigurable arrays (CGRAs), and emerging memory-centric and heterogeneous platforms—have emerged as promising solutions to address these challenges by balancing flexibility, adaptability, and power efficiency. This review systematically examines recent advances in reconfigurable Edge AI architectures for healthcare IoT, highlighting key trends in hardware–software co-design, AI-assisted design automation, memory-centric optimization, and domain-specific overlays. It further identifies critical challenges, including energy–performance trade-offs, runtime reconfiguration overheads, security and privacy vulnerabilities, limited standardization, and reliability concerns in dynamic clinical settings. Finally, future research directions are outlined, emphasizing self-optimizing and context-aware architectures, secure and trustworthy reconfiguration mechanisms, unified frameworks for heterogeneous healthcare workloads, and sustainable, carbon-aware edge computing. Collectively, this review positions energy-efficient reconfigurable architectures as a foundational enabler for next-generation Edge AI in IoT-enabled healthcare systems.