The genetic algorithm (GA) is an optimization metaheuristic that relies on the evolution of a set of solutions (population) according to genetically inspired transformations. In the variant of this technique called cellular GA, the evolution is done separately for subgroups of solutions. This paper describes a hardware framework capable of efficiently supporting custom accelerators for this metaheuristic. This approach builds a regular array of problem-specific processing elements (PEs), which perform the genetic evolution, connected to shared memories holding the local subpopulations. To assist the design of the custom PEs, a methodology based on highlevel synthesis from C++ descriptions is used. The proposed architecture was applied to a spectrum allocation problem in cognitive radio networks. For an array of 5×5 PEs in a Virtex-6 FPGA, the results show a minimum speedup of 22× compared to a software version running on a PC and a speedup near 2000× over a MicroBlaze soft processor.
展开▼
