Recently wearable EEG monitoring devices with seizure detection processor using convolutional neural network (CNN) have been proposed to detect the seizure onset of patients in real time for alert or stimulation purpose. High energy efficiency and accuracy are required for the seizure detection processor due to the tight energy constraint of wearable devices. However, the use of CNN and multi-channel processing nature of seizure detection result in significant energy consumption. In this work, an energy-efficient seizure detection processor is proposed, featuring multi-stage CNN classification, segmented data processing and adaptive channel selection to reduce the energy consumption while achieving high accuracy. The design has been fabricated and tested using a 55nm process technology. Compared with several state-of-the-art designs, the proposed design achieves the lowest energy per classification (0.32 μJ) with high sensitivity (97.78%) and low false positive rate per hour (0.5).

In the recent years, the spiking neural network (SNN) has attracted increasing attention due to its low energy consumption and online learning potential. However, the design of SNN processor has not been thoroughly investigated in the past, resulting in limited performance and energy consumption. In this work, a fast and energy-efficient SNN processor with adaptive clock/event-driven computation scheme and online learning capability has been proposed. Several techniques have been proposed to reduce the computation time and energy consumption, including Adaptive Clock- and Event-Driven Computing Scheme, Neighboring PE Borrowing Technique, Compressed Spike Routing Technique and Reconfigurable PE for Inference and Learning. Implemented on a Virtex-7 FPGA, the proposed design achieves computation time of 3.15 ms/image, inference energy consumption of$0.028~\mu $J/synapse/image and online learning energy consumption of$0.297~\mu $J/synapse/image for the MNIST 10-class dataset, which outperform several state-of-the-art SNN processors. The proposed SNN processor is suitable for real-time and energy-constrained applications.