Int J Performability Eng ›› 2017, Vol. 13 ›› Issue (6): 854-863.doi: 10.23940/ijpe.17.06.p7.854863

• Original articles • Previous Articles     Next Articles

HACO-F: An Accelerating HLS-Based Floating-Point Ant Colony Optimization Algorithm on FPGA

Shuo Zhanga, b, Zhangqin Huanga, b, *, Weidong Wanga, b, *, Rui Tiana, b, and Jian Hea, b   

  1. aBeijing Advanced Innovation Center for Future Internet Technology, Beijing University of Technology, Beijing 100124,China
    bBeijing Engineering Research Center for IOT Software and Systems, Beijing University of Technology, Beijing 100124,China

Abstract: 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%.

Submitted on July 25, 2017; Revised on August 30, 2017; Accepted on September 15, 2017(This paper was presented at the Third International Symposium on System and Software Reliability.
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