Abstract
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.
Highlights
Reconfigurable custom computing machines are an effective mean to accelerate critical algorithms that require to be executed under strict timing constraints
We have evaluated the behaviour of the convergence of the algorithm for the different configurations and measured the speedup achieved by the different organizations of the cellular genetic algorithm processor (cGAP)
To build the cGAP we start by describing the processing elements (PEs) functionality in C++ of the operations needed to generate a new solution, which are described in Section 5.2, which will be synthesized with the high-level synthesis (HLS) flow referred above
Summary
Reconfigurable custom computing machines are an effective mean to accelerate critical algorithms that require to be executed under strict timing constraints. The genetic algorithm (GA) is a population-based metaheuristic where a population constituted by a set of feasible solutions of an optimization problem goes through an evolutionary process inspired by the biological evolution of living species, in order to improve their quality (Holland 1975). This metaheuristic relies on mimicking operations that are observed in nature, as the genetics-inspired operations crossover, mutation, and natural selection. Both the selection and replacement operations choose solutions according to an evolution strategy that takes into account their fitness values to promote the
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