A Reconfigurable Custom Machine for Accelerating Cellular Genetic Algorithms

• Type: Article in International Scientific Journal
• Date: 2018

Authors

Santos, PV (Author) Other; José Carlos Alves (Author) FEUP; João Canas Ferreira (Author) FEUP

Journal

Title: U.Porto Journal of Engineering

Pages: 2-13

Publisher: Universidade do Porto. Faculdade de Engenharia

Other information

Authenticus ID: P-00T-RZC

DOI: 10.24840/2183-6493_002.002_0002

Abstract (EN): <jats:p>In this work we present a reconfigurable and scalable custom processor array for solving optimization problems using cellular genetic algorithms (cGAs), based on a regular fabric of processing nodes and local memories. Cellular genetic algorithms are a variant of the well-known genetic algorithm that can conveniently exploit the coarse-grain parallelism afforded by this architecture. To ease the design of the proposed computing engine for solving different optimization problems, a high-level synthesis design flow is proposed, where the problem-dependent operations of the algorithm are specified in C++ and synthesized to custom hardware. A spectrum allocation problem was used as a case study and successfully implemented in a Virtex-6 FPGA device, showing relevant figures for the computing acceleration.</jats:p>

Language: English

Type (Professor's evaluation): Scientific