FCCM '15 practice talk

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• Liucheng Guo (Imperial College)
• Tuesday 31 March 2015, 13:00-14:00
• Level 9 Mahanakorn Lab., EE Dept..

If you have a question about this talk, please contact Grigorios Mingas.

Genetic Algorithms (GAs) are a class of numerical and combinatorial optimisers which are especially useful for complex non-linear and non-convex problems. However, the required compute time often limits their application to large-scale or latency-insensitive problems, so techniques to increase the computational efficiency of GAs are needed. FPGA -based acceleration has great potential for speeding up genetic algorithms, but existing FPGA G As are limited by the generation focused approaches inherited from software GAs. Many parts of the generational approach do not map well to hardware, such as the large shared population memory and intrinsic loop-carried dependency. To address this problem, this paper proposes a new hardware oriented approach to GAs, called Pipelined Genetic Propagation (PGP), which is intrinsically distributed and pipelined. PGP represents a GA solver as a graph of loosely coupled genetic operators, which allows the solution to be scaled to the available resources, and also to dynamically change topology at run-time to explore different solution strategies. Experiments show that pipelined genetic propagation is effective in solving seven different applications. Our proposed work is 5 times faster than a recent FPGA -based GA system, and 90 times faster than a CPU -based GA system.

This talk is part of the CAS Talks series.

This talk is included in these lists:

• Andrea Picciau's list
• CAS Talks
• Circuits and Systems Group: Internal Seminars
• Level 9 Mahanakorn Lab., EE Dept.

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