Abstract: Compared with traditional methods,the CNN-based scene matching algorithm has higher matching accuracy,better adaptability and stronger anti-interference ability.However,the algorithm has massive computing and storage requirements,which makes it difficult to deploy at the edge.To improve the real-time computing,an efficient edge-side acceleration scheme is designed and implemented.On the basis of analyzing the computation characteristics and overall architecture of the algorithm,correlation specific accelerator(CSA) is designed based on Winograd fast convolution method,and the acceleration scheme using CSA and deep-learning processor unit(DPU) pipelined computing feature correlation layer and feature extraction network is proposed.Experiments on Xilinx's ZCU102 development board finds that the peak perfor-mance of CSA reaches 576 GOPS,the actual performance reaches 422.08 GOPS,and the DSP usage efficiency reaches 4.5 Operation/clock.The peak performance of the accele-ration system reaches 1 600 GOPS,and the throughput delay of the algorithm is reduced to 157.89 ms.Experimental results show that the acceleration scheme can efficiently utilize the computing resources of the FPGA,to realize the real-time computing of the CNN-based scene matching algorithm.

Key words: Acceleration computing, Scene matching algorithm, Deep learning, FPGA, Winograd algorithm, Specific accelerator