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.