In this paper we discuss and analyze the FPGA-based implementation of an algorithm for the Traveling Salesman Problem (TSP), and in particular of 2-Opt, one of the most famous local optimization algorithms, for Euclidean TSP instances up to a few hundred cities. We introduce the notion of "symmetrical 2-Opt moves" which allows us to uncover fine-grain parallelism when executing the specified algorithm. We propose a novel architecture that exploits this parallelism, and demonstrate its implementation in reconfigurable hardware. We evaluate our proposed architecture and its implementation on a state-of-the-art FPGA using a subset of the TSPLIB benchmark, and find that our approach exhibits better quality of final results and an average speedup of 600% when compared with the state-of-the-art software implementation. Our approach produces, to the best of our knowledge, the fastest to date TSP 2-Opt solver for small-scale Euclidean TSP instances.
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