HW/SW co-design of reconfigurable hardware-based genetic algorithm in FPGAs applicable to a variety of problems

This paper describes the implementation of a reconfigurable hardware-based genetic algorithm (HGA) accelerator using the hardware-software (HW/SW) co-design methodology. This HGA is coupled with a unique TRNG that extracts random jitters from a phase lock loop (PLL) to ensure proper GA operation. It...

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Veröffentlicht in:Computing 2013-09, Vol.95 (9), p.863-896
Hauptverfasser: Nambiar, Vishnu P., Balakrishnan, Sathivellu, Khalil-Hani, Mohamed, Marsono, M. N.
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container_end_page 896
container_issue 9
container_start_page 863
container_title Computing
container_volume 95
creator Nambiar, Vishnu P.
Balakrishnan, Sathivellu
Khalil-Hani, Mohamed
Marsono, M. N.
description This paper describes the implementation of a reconfigurable hardware-based genetic algorithm (HGA) accelerator using the hardware-software (HW/SW) co-design methodology. This HGA is coupled with a unique TRNG that extracts random jitters from a phase lock loop (PLL) to ensure proper GA operation. It is then applied and benchmarked with several case studies, which include the optimization of a simple fitness function, a constrained Michalewicz function, and the tuning of parameters in finger-vein biometrics. A HGA solution is necessary in systems that demand high performance during the optimization process. However, implementations that are completely designed in hardware will result in a very rigid architecture, making it difficult to reconfigure the system for use in different applications. This paper aims to solve this issue by proposing a HGA design that provides reconfigurability and flexibility by moving problem-dependent processes into software. The prototyping platform used is an Altera Stratix II EP2S60 FPGA prototyping board with a clock frequency of 50 MHz. The HW/SW co-design technique is applied, and system partitioning is done based on aspects such as system constraints, operational intensity, process sequencing, hardware logic utilization, and reconfigurability. Experimental results show that the proposed HGA outperforms equivalent software implementations compiled with an open-sourced C++ GA component library (GAlib) running on the same prototyping platform by 102 times at most. In the final case study, the application of the proposed HGA in tunable parameter optimization in finger-vein biometrics improved the matching rate, reducing the equal error rate (EER) value from 1.004% down to 0.101%.
doi_str_mv 10.1007/s00607-013-0305-5
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N.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>HW/SW co-design of reconfigurable hardware-based genetic algorithm in FPGAs applicable to a variety of problems</atitle><jtitle>Computing</jtitle><stitle>Computing</stitle><date>2013-09-01</date><risdate>2013</risdate><volume>95</volume><issue>9</issue><spage>863</spage><epage>896</epage><pages>863-896</pages><issn>0010-485X</issn><eissn>1436-5057</eissn><abstract>This paper describes the implementation of a reconfigurable hardware-based genetic algorithm (HGA) accelerator using the hardware-software (HW/SW) co-design methodology. This HGA is coupled with a unique TRNG that extracts random jitters from a phase lock loop (PLL) to ensure proper GA operation. It is then applied and benchmarked with several case studies, which include the optimization of a simple fitness function, a constrained Michalewicz function, and the tuning of parameters in finger-vein biometrics. 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source SpringerNature Journals; EBSCOhost Business Source Complete
subjects Artificial Intelligence
Biometrics
Case studies
Chromosomes
Co-design
Computer & video games
Computer Appl. in Administrative Data Processing
Computer Communication Networks
Computer engineering
Computer peripherals
Computer Science
Computers
Design techniques
Embedded systems
Evolution & development
Field programmable gate arrays
Game theory
Genetic algorithms
Image processing systems
Information Systems Applications (incl.Internet)
Linux
Mathematical models
Open source software
Optimization
Platforms
Prototyping
Software
Software Engineering
Studies
title HW/SW co-design of reconfigurable hardware-based genetic algorithm in FPGAs applicable to a variety of problems
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