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In this paper, a novel accelerating Ant Colony Optimization (ACO) algorithm based on High-Level Synthesis (HLS) on FPGA (Field Programmable Gate Array) is proposed. The proposed algorithm (HACO-F) is implemented by C/C++ programming language and calculated by floating-point. For the sake of accelerating, the algorithm mainly employs the data optimization strategy to redefine the variables precision in HACO-F to reduce resource utilization and energy consumption. Then, we explore a loop optimization strategy including pipeline and unroll to parallelize loops in HACO-F to decrease computation time. The experimental results show that the HACO-F algorithm can achieve more than 6 times accelerating performance than that of the AS (Ant System) at the same search ability. The resource utilization in HACO-F is 1% FF, 4% LUT, and 9% BRAM decrease. The total on-chip energy consumption of HACO-F is reduced by 23.9%. © 2017 Totem Publisher, Inc. All rights reserved.
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