An Edge AI Accelerator Design Based on HDC Model for Real-time EEG-based Emotion Recognition System with RISC-V FPGA Platform

The rapid growth of AI and IoT has transformed healthcare through emotion recognition using physiological signals like EEG, promising applications in clinical psychology, human-computer interaction, and personalized healthcare. However, the challenge of real-time emotion recognition requires effective solutions for hardware cost and computational speed. This paper proposes an edge AI accelerator design based on the Hyperdimensional Computing (HDC) model, utilizing a FPGA and RISC-V platform for real-time emotion recognition system using EEG signals. The HDC model offers benefits in power efficiency and computational complexity compared to traditional neural networks, making it suitable for resource-constrained IoT devices and edge computing. The proposed hyperdimensional computing model achieved high accuracy in the analysis of emotion from 17-channel EEG data, with 79.04% accuracy for valence and 85.95% accuracy for arousal. Additionally, our hardware design achieved 500 MHz and 42.69 nJ/prediction in TSMC 16 nm technology simulation, which is 2.1 times energy efficiency improvement than traditional AI.