This paper presents how field-programmable gate arrays (FPGAs) are used to accelerate the Sequential Monte Carlo method for air traffic management. A novel data structure is introduced for a particle stream that enables efficient evaluation of constraints and weights. A parallel implementation for this streaming data structure is designed, and an analytical model is provided for estimating the performance and resource usage of our implementation. We compare our design to implementations on CPU and GPU. We show 9.3 times speed up and 89 times improvement in energy efficiency over an Intel Core i7-950 CPU with 8 threads and demonstrate 1.3 times speed up and 13.5 times improvement in energy efficiency over an NVIDIA Tesla C2070 GPU with 448 cores. We also estimate the performance of FPGA in future scenario and show that FPGA is able to control 15 times and 2.8 times more aircraft than CPU and GPU in real-time respectively.

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