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Energy-Efficient FPGA Implementation of an End-to-End Cosine-Domain CNN Using High-Level Synthesis
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)
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