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Field-programmable gate array (FPGA) provides rich parallel computing resources with high energy efficiency, making it ideal for deep convolutional neural network (CNN) acceleration. In recent years, automatic compilers have been developed to generate network-specific FPGA accelerators. However, with more cascading deep CNN algorithms adapted by various complicated tasks, reconfiguration of FPGA devices during runtime becomes unavoidable when network-specific accelerators are employed. Such reconfiguration can be difficult for edge devices. Moreover, network-specific accelerator means regeneration of RTL code and physical implementation whenever the network is updated. This is not easy for CNN end users. In this article, we propose a domain-specific FPGA overlay processor, named OPU to accelerate CNN networks. It offers software-like programmability for CNN end users, as CNN …