A general-purpose framework for FPGA-accelerated genetic algorithms. (2015)

• Record Type: Journal Article
• Authors: Guo, Liucheng; Funie, Andreea Ingrid; Xie, Zhongliu; Thomas, David; Luk, Wayne
• Is Part Of: International journal of bio-inspired computation. Volume 7:Number 6(2015)
• Page Start: 361
• Page End: 375
• Publication Date: 2015
• Subjects: genetic algorithms -- field programmable gate arrays -- FPGA -- automated framework -- general-purpose framework -- scalable hardware architectures -- customisable hardware architectures; Biologically-inspired computing -- Periodicals; Computational biology -- Periodicals; 572.0285
• Languages: English
• ISSNs: 1758-0366

Abstract

FPGA-based genetic algorithms (GAs) can effectively optimise complex applications, but require extensive hardware architecture customisation. To promote these accelerated GAs to potential users without hardware design experience, this study proposes a general-purpose automated framework for creating and executing a GA system on FPGAs. This framework contains scalable and customisable hardware architectures while providing a unified platform for different chromosomes. At compile-time, only a high-level input of the target application needs to be provided, without any hardware-specific code being necessary. At run-time, application inputs and GA parameters can be tuned, without time-consuming recompilation, for finding further good configurations of GA execution. The framework was tested on a high performance FPGA platform using nine problems and benchmarks, including the travelling salesman problem, a locating problem and the NP-hard set covering problem. Experiments show the system's flexibility and an average speedup of 29 times over a multi-core CPU.