RVDLAHA:用于可穿戴应用中设备上实时癫痫发作检测和个性化的 RISC-V DLA 硬件架构。

Shuenn-Yuh Lee, Ming-Yueh Ku, Yen-Hsing Tsai, Chou-Ching Lin
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引用次数: 0

摘要

癫痫是一种遍布全球的慢性神经系统疾病,可能会在毫无征兆的情况下对生命造成威胁。因此,使用可穿戴设备对癫痫进行实时检测和治疗至关重要。此外,针对个人用户的个性化疾病检测算法也是临床应用中的一项挑战。一些研究提出了利用卷积神经网络(CNN)和可编程硬件架构加速 CNN 推断过程的癫痫发作检测算法。然而,个性化癫痫发作检测算法仍无法在这些硬件架构上实现。因此,本研究提出了应对挑战的三大贡献:实时癫痫发作检测和个性化算法、可编程精简指令集计算机-V(RISC-V)深度学习加速器(DLA)硬件架构(RVDLAHA)和专用 RISC-V DLA(RVDLA)编译器。在以实验鼠为对象的动物实验中,所提出的基于 CNN 的癫痫发作检测算法在 32 位浮点时的准确率达到 99.5%,在 16 位定点时的准确率达到 99.3%。此外,所提出的个性化算法还将不同数据库的检测准确率从 85.0% 提高到 92.9%。RVDLAHA 在 Xilinx PYNQ-Z2 上实现,工作频率为 1 MHz 时功耗仅为 0.107 W。每个步骤,包括原始数据输入、预处理、检测和个性化,分别只需要 17.8、1.0、1.1 和 1.3 毫秒。利用该硬件架构,癫痫发作检测和个性化算法可提供设备上的实时监测。
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RVDLAHA: An RISC-V DLA Hardware Architecture for On-Device Real-Time Seizure Detection and Personalization in Wearable Applications.

Epilepsy is a globally distributed chronic neurological disorder that may pose a threat to life without warning. Therefore, the use of wearable devices for real-time detection and treatment of epilepsy is crucial. Additionally, personalizing disease detection algorithms for individual users is also a challenge in clinical applications. Some studies have proposed seizure detection algorithms with convolutional neural networks (CNNs) and programmable hardware architectures for speeding up the process of CNN inference. However, personalizing seizure detection algorithms could still not be performed on these hardware architectures. Consequently, this study proposes three key contributions to address the challenges: a real-time seizure detection and personalization algorithm, a programmable reduced instruction set computer-V (RISC-V) deep learning accelerator (DLA) hardware architecture (RVDLAHA), and a dedicated RISC-V DLA (RVDLA) compiler. In animal experiments with lab rats, the proposed CNN-based seizure detection algorithm obtains an accuracy of 99.5% for a 32-bit floating point and an accuracy of 99.3% for a 16-bit fixed point. Additionally, the proposed personalization algorithm increases the testing accuracy across different databases from 85.0% to 92.9%. The RVDLAHA is implemented on Xilinx PYNQ-Z2, with a power consumption of only 0.107 W at an operating frequency of 1 MHz. Each step, including raw data input, preprocessing, detection, and personalization, requires only 17.8, 1.0, 1.1, and 1.3 ms, respectively. With the hardware architecture, the seizure detection and personalization algorithm can provide on-device real-time monitoring.

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