An Energy-efficient and Programmable RISC-V CNN Coprocessor for Real-time Epilepsy Detection and Identification on Wearable Devices

This paper has proposed an energy-efficient epilepsy detection framework for embedded systems. The epilepsy detection framework is implemented in 11 layers Convolution Neural Network (CNN) with a 2-stage RISC-V core and a coprocessor to accelerate CNN inferences. The CNN algorithm provides 97.8% and 93.5% accuracy on floating-point and fixedpoint operations respectively. The proposed CNN coprocessor is designed to offload CNN inference from RISC-V core to hardware with 51 nJ data transfer energy and 0.9 µJ inference energy for each 500 points input data frame. The coprocessor reduces the runtime of CNN inferences over 10^6x to perform only 0.012 s latency for each classification. According to the energy-efficient coprocessor, an AI-based solution is practical for real-time epilepsy detection on wearable devices for consumer electronics.