In order to meet the requirements of deploying Convolutional Neural Network(CNN)on embedded de-vices to realize real-time image acquisition and classification,traditional CNN has many parameters,large computa-tion,slow reasoning speed and large power consumption when deployed on CPU and GPU.This paper presents a de-sign scheme of Mobilenet V2 lightweight convolutional neural network classifier based on FPGA platform.Using Cameralink camera to capture images,the image preprocessing methods of cutting,ping-pong cache and quantization are designed to achieve continuous image acquisition.Each layer of CNN occupies resources and computing struc-ture respectively to achieve continuous image processing.A pipeline structure of PW and DW is designed,and the sparsity calculation optimization strategy of fully connected layer is designed to reduce the computation amount and processing delay.The classification time of single image is 1.25ms,and the energy consumption ratio is 14.50GOP/s/W.
万方数据
维普
