Three-dimensional convolutional neural networks (3D CNNs) show considerable promise for lung nodule detection. However, their high computational complexity and memory demands present substantial challenges for acceleration on a single field-programmable gate array (FPGA). To address this, we propose efficient mapping schemes for a multi-FPGA platform, leveraging its massive parallelism to maximize computational efficiency. Our system, integrating six customized FPGA boards, achieves state-of-the-art performance, delivering approximately 15.9 tera operations per second (TOPS) for nodule segmentation and approximately 3.8 TOPS for nodule classification. Compared to a central processing unit baseline, it achieves a 128.2× speedup while exhibiting 6.7× higher energy efficiency than a graphics processing unit implementation. Furthermore, the system attains a state-of-the-art recall rate of 87.1% on the real-world clinical benchmark.
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