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. ... Read more

Electrophysiological recordings of neural activity in a mouse's brain are very popular among neuroscientists for understanding brain function. One particular area of interest is acquiring recordings from the Purkinje cells in the cerebellum in order to understand brain injuries and the loss of motor functions. However, current setups for such experiments do not allow the mouse to move freely and, thus, do not capture its natural behaviour since they have a wired connection between the animal's head stage and an acquisition device. In this work, we propose a lightweight neuronalspike detection and classification architecture that leverages on the unique characteristics of the Purkinje cells to discard unneeded information from the sparse neural data in real time. This allows the (condensed) data to be easily stored on a removable storage device on the head stage, alleviating the need for wires. Synthesis results reveal a >95% overall classification accuracy while still resulting in a small-form-factor design, which allows for the free movement of mice during experiments. Moreover, the power-efficient nature of the design and the usage of STT-RAM (Spin Transfer Torque Magnetic Random Access Memory) as the removable storage allows the head stage to easily operate on a tiny battery for up to approximately 4 days. ... Read more