FPGA-Enabled, Smart Implants: A Path to Personalized Healthcare

Goal: Contrary to the conventional view that Field-Programmable Gate Arrays (FPGAs) are poorly suited to the stringent energy and resource constraints of Implantable Medical Devices (IMDs), this work investigates their potential to improve computational performance and energy efficiency in this domain. We propose heterogeneous CPU–FPGA systems as a means to support the growing demand for embedded AI in next-generation IMDs for intelligent and personalized healthcare. Methods: We evaluated heterogeneous CPU–FPGA architectures against CPU-only configurations across representative AI-driven neural and security IMD workloads. Results: FPGAs achieved lower execution latency and improved energy efficiency than CPU-based implementations in computationally intensive neural workloads, despite higher instantaneous power consumption in some cases due to parallel execution. In low-duty-cycle security workloads where aggressive power gating is feasible, FPGA- and CPU-based solutions exhibited comparable overall performance. Conclusions: These findings demonstrate that heterogeneous CPU–FPGA systems can integrate advanced AI and security algorithms into IMDs while satisfying battery-life, power-density, real-time processing, and practical implantability constraints relevant to long-term medical-device deployment, enabling intelligent, personalized, and secure implantable healthcare.