Hardware-aware knowledge-distilled CNN for real-time classification of plaque-associated angiographic findings on FPGA.

Coronary artery disease (CAD) is one of the leading causes of global mortality, necessitating accurate assessment of coronary artery stenosis and plaque-associated angiographic findings from coronary angiography images. Although deep learning-based diagnostic models have demonstrated high predictive capability, their deployment on resource-constrained embedded platforms remains challenging because of computational complexity and memory requirements. To address these limitations, this study proposes a knowledge-distillation-based hardware-aware framework that transfers discriminative knowledge from a high-capacity teacher network to a lightweight convolutional neural network (CNN). The proposed framework integrates software-level localization of plaque-associated angiographic findings with FPGA-oriented optimization. Using a 90% training and 10% testing split, the proposed model achieved an accuracy of 98.64%, precision of 99.06%, recall of 97.82%, PPV of 0.99, NPV of 0.98, MCC of 0.96, and an AUC of 0.9940. And under 10-fold cross-validation, the model achieved a mean accuracy of 97.53% ± 0.27%, precision of 97.82%, recall of 96.94%, PPV of 0.98, NPV of 0.97, MCC of 0.95, and a mean AUC of 0.9897, which confirms that our proposed model exhibits high robust performance. For hardware realization, the optimized student CNN was deployed on a Xilinx Zynq UltraScale + MPSoC FPGA using the Vitis High-Level Synthesis (HLS) toolchain. The implemented accelerator achieved a kernel-level inference latency of 10.6 µs and a Peak On-Chip Kernel Throughput of approximately 94,339 inferences/s, while maintaining balanced hardware resource utilization and low power consumption. Overall, the proposed framework demonstrates accurate classification of plaque-associated angiographic findings with efficient FPGA deployment.