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). The proposed architecture performs intermediate feature extraction using real-valued DCT-domain representations, avoiding complex-valued arithmetic and intermediate inverse transforms between spect…

Source: arXiv cs.AR (Hardware Architecture), published 2026-10-30. The summary is the Hub's own; the full story is at the source. Report a rights or accuracy concern