中国海洋大学学报（自然科学版）2024，Vol.54Issue(8)：152-165,14.DOI:10.16441/j.cnki.hdxb.20230110
基于深度可分离卷积神经网络的水声目标分类研究及FPGA实现
Underwater Acoustic Target Classification Based on Depthwise Separable Convolution Neural Network and FPGA Implementation
摘要
Abstract
Aiming at the problem that the traditional sonar processor has limited computing power and low energy efficiency ratio,which is difficult to support real-time deduction of underwater acoustic tar-get recognition,this paper designs a real-time computing and processing system for passive sonar under-water targets based on heterogeneous SoC platform.This system provides an efficient accelerator solu-tion with low resource cost and nearly lossless network accuracy to reduce deduction delay and power consumption.This paper optimizes the structure of MobileNetV1,implements its forward deduction process on the field programmable logic gate array(FPGA)through the parallel pipeline acceleration structure,and binarizes its weight parameters,so as to reduce the amount of storage and computation while speeding up its deduction speed.In addition,according to the optimization idea of block parallel in channel dimension and input image height,a pipeline optimization strategy with depthwise separable convolution is designed to reduces the time of forward deduction.The experiment shows that using the underwater acoustic data set collected at sea,the recognition accuracy of the system realized in this pa-per is 0.875,the time delay is 4.23 ms on the image with a resolution of 3×128×128.Compared with the CPU speed,it is 70.68 times faster,which is 68%of the GPU speed.The power consumption is 10.6%of CPU and 1.44%of GPU respectively.This paper provides a design idea for the application and deployment of neural networks on lightweight mobile terminals or edge devices with limited hard-ware resources and power consumption,and for promoting the construction of integrated underwater ex-ploration networks and rapid acquisition of underwater information.关键词
水声目标分类/深度可分离卷积/定点量化/FPGAKey words
underwater acoustic target classification/depthwise separable convolution/fixed-point quantization/FPGA分类
信息技术与安全科学引用本文复制引用
张天帅,刘金涛,王良..基于深度可分离卷积神经网络的水声目标分类研究及FPGA实现[J].中国海洋大学学报（自然科学版）,2024,54(8):152-165,14.基金项目
国家自然科学基金项目(52001296)资助 Supported by the National Natural Science Foundation of China(52001296) （52001296）
