A Parallel Genetic Algorithm for Placement and Routing on Cloud Computing Platforms
The design and implementation of today's most advanced VLSI circuits and multi-layer printed circuit boards would not be possible without automated design tools that assist with the placement of components and the routing of connections between these components. In this work, we investigate how...
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| Format: | Others |
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VCU Scholars Compass
2011
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| Online Access: | http://scholarscompass.vcu.edu/etd/2406 http://scholarscompass.vcu.edu/cgi/viewcontent.cgi?article=3405&context=etd |
| Summary: | The design and implementation of today's most advanced VLSI circuits and multi-layer printed circuit boards would not be possible without automated design tools that assist with the placement of components and the routing of connections between these components. In this work, we investigate how placement and routing can be implemented and accelerated using cloud computing resources. A parallel genetic algorithm approach is used to optimize component placement and the routing order supplied to a Lee's algorithm maze router. A study of mutation rate, dominance rate, and population size is presented to suggest favorable parameter values for arbitrary-sized printed circuit board problems. The algorithm is then used to successfully design a Microchip PIC18 breakout board and Micrel Ethernet Switch. Performance results demonstrate that a 50X runtime performance improvement over a serial approach is achievable using 64 cloud computing cores. The results further suggest that significantly greater performance could be achieved by requesting additional cloud computing resources for additional cost. It is our hope that this work will serve as a framework for future efforts to improve parallel placement and routing algorithms using cloud computing resources. |
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