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).

Recent development of the processing-in-memory (PIM) architecture has demonstrated high efficiency by reducing data movements. However, the performance of the conventional PIM architecture is limited by several issues, including frequent bit-line operations, complicated control of data flow, and massive inter-macro data movements. In addition, both analog- and digital-PIM solutions have obstacles to meet requirement of high-precision computation. In this work, we explore the tradeoff between data movement and energy efficiency of PIM architecture. We develop a PIM architecture, namely ReverSearch, to accelerate multiple-and-accumulate operation, equipped with reverse searching engine and look up table operations. Also, the corresponding data mapping and data flow methods are provided to improve the performance of the ReverSearch architecture. Based on our evaluation, ReverSearch improves the energy efficiency by 17.26 × and 3.68 ×, compared to the baseline of LUT-Cache [1] and LAcc [2].