Efficient CNN Architecture on FPGA Using High Level Module for Healthcare Devices

Abstract

Modern wearable healthcare devices require new technologies with resource efficiency in terms of high performance, low energy consumption and diagnostic accuracy. In the field of artificial intelligence, the convolutional neural network (CNN) has performed as an effective algorithm. Field-programmable gate arrays (FPGAs) have been extensively utilised to construct hardware accelerators for CNNs. This paper suggests using an accelerator to create a specific 1-D CNN to classify the electrogram (ExG). ExGs used here include electrocardiogram, electroencephalogram and electromyography. The pipelined structure is designed with a register in the middle to facilitate easy data transfer. A 1-D CNN using an accelerator to categorise ExG signals implemented on Xilinx Zynq xc7z045 platform outperforms FPGA peer applications on the same platform by $1.14\times $ in terms of speed. In addition, the 1-D CNN proposed accelerator operates very efficiently due to the use of a tristate buffer in the multiplexer and the substitution of the shift for the multiplier, resulting in a resource-efficient accelerator with 161 GOP/s/W energy efficiency and 28 GOP/s/KLUT, an improvement of 1.67 over the previous model. Finally, the performance of the accelerator applied to a Xilinx Zynq xc7z045 FPGA operating at 442.948MHz was calculated, achieving 1.145 TFLOP/s.

Keywords

• Electrogram classification
• convolutional neural network (CNN)
• FPGA
• healthcare monitoring
• hardware accelerator