Low-Power and Low-Cost AI Processor With Distributed-Aggregated Classification Architecture for Wearable Epilepsy Seizure Detection

Wearable devices with continuous monitoring capabilities are critical for the daily detection of epileptic seizures, as they provide users with accurate and comprehensible analytical results. However, current AI classifiers rely on a two-stage recognition process for continuous monitoring, which only reduces operation time but remains challenged by the high cost of additional hardware. To address this problem, this article proposes a novel fusion architecture for AI processors, which enables event-triggered cross-paradigm integration and computation. Our method introduces a distributed-aggregated classification architecture (D-ACA) that facilitates the reuse of hardware resources across two-stage recognition, thereby obviating the need for standby hardware and enhancing energy efficiency. Integrating a non-encoding biomedical circuit method based on spiking neural networks (SNNs), the architecture eliminates encoded neurons at the hardware level, significantly optimizing energy consumption and hardware resource utilization. Additionally, we develop a configurable and highly flexible control method that supports various neuron modules, enabling continuous detection of epileptic seizures and activating high-precision recognition upon event detection. Finally, we implement the design on the Xilinx ZCU 102 FPGA board, where the AI processor achieves a high classification accuracy of 98.1% while consuming extremely low classification energy (3.73 $\mu$J per classification).