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As the complexity of FPGA architectures increases, there is a raising need to improved productivity and performance in several computing domains such as image processing, financial analytics, edge computing and deep learning. However, vendor tools are mostly general-purpose as they attempt to provide an acceptable quality of result (QoR) on a broad set of applications, which may not exploit application/domain-specific characteristics to deliver higher QoR. In this paper, we present a divide-and-conquer design flow that enables application/domain-specific optimization on the design of convolutional neural network (CNN) architectures on Xilinx FPGAs. The proposed approach follows three fundamental steps; Step 1: Break the design down into components, Step 2: Implement these separate components, and Step 3: Efficiently generate the final design by assembling pre-built components with minimal QoR lost. Recent research has even demonstrated that such approaches may provide better QoR than that of the traditional Vivado flow in some instances [1], [2]. By pre-implementing specific components of a design, higher performance can be achieved locally and maintained to a certain extent when assembling the final circuit. This approach is supported by two main observations [1]: (1) vendor tools such as Vivado tend to deliver high performance results on small modules in a design. (2) Computing applications such as machine learning designs increase in size by replicating modules. CNN inference refers to the forward propagation of M input images through L layers. The repetition of components within CNN architectures make them suitable candidates for RapidWright implementation as the CNN sub-modules can be optimized for performance in standalone, and the achieved performance can be preserved when replicating and relocating the modules across the FPGA.