• Vina-FPGA2 introduces an inter-module pipeline design that overcomes the irregular computation bottlenecks of AutoDock Vina, achieving significant performance gains.
• It exploits task-level independence and a reinforcement learning-based resource allocation solver for efficient hardware implementation on FPGA.
• On Xilinx UltraScale XCKU060, Vina-FPGA2-Enhanced achieves 12.6× speedup over CPU and 3.3× improvement over Vina-FPGA.
• Compared to Vina-GPU, Vina-FPGA2 improves energy efficiency by 7.2×, making it a compelling low-power accelerator for molecular docking.
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