Epilepsy is a common clinical disease. Severe epilepsy can be life-threatening in certain unexpected conditions, so it is important to detect seizures instantly with a wearable device and to provide treatment within the golden window. The observation of the electroencephalography (EEG) signal is an imperative method to assist correct epilepsy diagnosis. To detect and classify EEG signals, a convolutional neural network (CNN) is an intuitive and appropriate method that borrows expertise from neurologists. However, the computational cost of training and inference on artificial intelligence (AI)-based solutions make software-only and hardware-only solutions incompetent for real-time monitoring on embedded devices. Hence, this study proposes three key contributions for the challenge, namely, an algorithm framework to provide real-time epilepsy detection, a dedicated coprocessor chip implementing this framework to enable real time epilepsy detection to offload and accelerate detection algorithm, and a custom interface with the coprocessor and reduced instruction set computer-V (RISC-V) instructions to reconfigure the coprocessor and transfer data. The epilepsy detection framework is implemented in 11-layer CNN. The proposed epilepsy detection algorithm performs 97.8% accuracy for floating-point and 93.5% for fixed-point operations through animal experiments with lab rats. The RISC-V CNN coprocessor is fabricated in the TSMC 0.18-μm CMOS process. For each classification, the coprocessor consumes 51 nJ/class. and 0.9 µJ/class. energy on data transfer and inference, respectively. The detection latency on the chip is 0.012 s. With the integration of the hardware coprocessor, AI algorithms can be applied to epilepsy detection for real-time monitoring.

中文翻译：

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用于可穿戴应用中实时癫痫检测的 RISC-V CNN 协处理器

癫痫是临床上常见的疾病。严重癫痫在某些意外情况下可能会危及生命，因此使用可穿戴设备立即检测癫痫发作并在黄金窗口内提供治疗非常重要。脑电图 (EEG) 信号的观察是辅助正确诊断癫痫的必要方法。为了检测和分类 EEG 信号，卷积神经网络 (CNN) 是一种直观且适当的方法，它借鉴了神经学家的专业知识。然而，基于人工智能 (AI) 的解决方案训练和推理的计算成本使得纯软件和纯硬件解决方案无法对嵌入式设备进行实时监控。因此，本研究提出了应对挑战的三个关键贡献，即，提供实时癫痫检测的算法框架、实现该框架的专用协处理器芯片以实现实时癫痫检测以卸载和加速检测算法，以及与协处理器和精简指令集计算机-V (RISC-V)的自定义接口重新配置协处理器和传输数据的指令。癫痫检测框架在 11 层 CNN 中实现。通过实验室大鼠的动物实验，所提出的癫痫检测算法对浮点运算的准确度为 97.8%，对定点运算的准确度为 93.5%。RISC-V CNN 协处理器采用 TSMC 0.18-μm CMOS 工艺制造。对于每个分类，协处理器消耗 51 nJ/类。和 0.9 µJ/级。分别用于数据传输和推理。芯片上的检测延迟为 0.012 s。