A 1-D CNN accelerator implemented on a Xilinx Zynq xc7z045 FPGA for electrogram classification outperformed peer applications by 1.14x in speed and achieved 161 GOP/s/W energy efficiency.
A novel 1-D CNN accelerator on FPGA demonstrates improved speed and energy efficiency for classifying electrogram signals in wearable healthcare devices.
Effect estimate: 1.14x speed improvement
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× 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.
Jameil et al. (Sat,) conducted a other in Electrogram (ExG) classification. 1-D CNN accelerator on Xilinx Zynq xc7z045 FPGA vs. FPGA peer applications / previous models was evaluated on Speed, energy efficiency, and resource efficiency (1.14x speed improvement). A 1-D CNN accelerator implemented on a Xilinx Zynq xc7z045 FPGA for electrogram classification outperformed peer applications by 1.14x in speed and achieved 161 GOP/s/W energy efficiency.