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In view of the timing-consuming and heavy workload problems that encountered when various convolutional neural network (CNN) models are deployed on different hardware to achieve algorithm acceleration, using the Tengine tool chain , an emerging deep learning compiler technology, to design a general deep learning accelerator that can efficiently and fastly connecting the network model and hardware backend. The deep learning accelerator’s platform was a ZYNQ’s ZCU104 development board, the idea of software and hardware co-design was used, the open source Nvidia Deep Learning Acceleator (NVDLA) is mapped on Field Programmable Gate Array (FPGA), and the SoC system was formed with ARM processor. NVDLA’s architecture is very standard, including software and hardware design, the Tengine tool chain is used to replace the original official compilation tool chain. After that, the network of lenet-5 and resnet-18 was realized on the built NVDLA platform, and the image classification task of the mnist and cifar-10 datasets was completed. Experimental results show that the Tengine toolchain is 2.5 times faster than NVDLA’s official compilation toolchain inference speed, and the quantitative tools are easy to use, and the network model deployment is efficient.