A Programmable CNN Accelerator with RISC-V Core in Real-Time Wearable Application

Abstract

This paper has proposed an epilepsy detection algorithm to identify the seizure attack. The algorithm includes a simplified signal preprocessing process and an 8 layers Convolution Neural Network (CNN). This paper has also proposed an architecture, including a CNN accelerator and a 2-stage reduced instruction set computer-V (RISC-V) CPU, to implement the detection algorithm in real-time. The accelerator is implemented in SystemVerilog and validated on the Xilinx PYNQ-Z2. The implementation consumes 3411 LUTs, 2262 flip-flops, 84 KB block random access memory (BRAM), and only 6 DSPs. The total power consumption is 0.118 W in 10-MHz operation frequency. The detection algorithm provides 99.16% accuracy on fixed-point operations with detection latency of 0.137 ms/class. Moreover, the CNN accelerator has the programable ability, so the accelerator can execute different CNN models to fit various wearable applications for different biomedical acquisition systems.