The computational cost of conventional Convolutional Neural Networks (CNNs) limits their deployment in low-power devices for high-speed edge AI applications. This paper presents an end-to-end cosine-domain CNN (CD-CNN) architecture for energy-efficient FPGA inference using high-level synthesis (HLS). The proposed architecture performs intermediate feature extraction using real-valued DCT-domain representations, avoiding complex-valued arithmetic and intermediate inverse transforms between spectral layers. The design is implemented on a Xilinx Kintex UltraScale+ XCKU5P FPGA and evaluated against a fully spectral Fourier-domain CNN (FD-CNN) under consistent network and implementation constraints. Experiments on the MNIST and 94-class ASCII datasets show that the CD-CNN reduces normalized resource utilization and improves inference throughput on both datasets. Its energy efficiency reaches 112.91 frames/J for MNIST and 101.27 frames/J for ASCII, corresponding to improvements of $1.51\times$ and $2.76\times$, respectively, over the fully spectral FD-CNN architecture. Furthermore, compared to the previous GPU-based fully spectral CD-CNN implementation, the proposed end-to-end FPGA implementation improves energy efficiency by $12.96\times$. These results demonstrate the potential of real-valued spectral processing in the DCT domain for energy-efficient edge AI deployment.

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