BibTex format
@inproceedings{Thomas:2015,
author = {Thomas, DB and Guo, L and Guo, C and Luk, W},
title = {Pipelined Genetic Propagation},
url = {http://hdl.handle.net/10044/1/21814},
year = {2015}
}
Publications
Citation
RIS format (EndNote, RefMan)
TY - CPAPER
AB - Genetic Algorithms (GAs) are a class of numericaland combinatorial optimisers which are especially useful forsolving complex non-linear and non-convex problems. However,the required execution time often limits their application to smallscaleor latency-insensitive problems, so techniques to increasethe computational efficiency of GAs are needed. FPGA-basedacceleration has significant potential for speeding up geneticalgorithms, but existing FPGA GAs are limited by the generationalapproaches inherited from software GAs. Many partsof the generational approach do not map well to hardware,such as the large shared population memory and intrinsic loopcarrieddependency. To address this problem, this paper proposesa new hardware-oriented approach to GAs, called PipelinedGenetic Propagation (PGP), which is intrinsically distributedand pipelined. PGP represents a GA solver as a graph ofloosely coupled genetic operators, which allows the solution to bescaled to the available resources, and also to dynamically changetopology at run-time to explore different solution strategies.Experiments show that pipelined genetic propagation is effectivein solving seven different applications. Our PGP design is 5 timesfaster than a recent FPGA-based GA system, and 90 times fasterthan a CPU-based GA system.
AU - Thomas,DB
AU - Guo,L
AU - Guo,C
AU - Luk,W
PY - 2015///
TI - Pipelined Genetic Propagation
UR - http://hdl.handle.net/10044/1/21814
ER -