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A Genetic Approach to Standard Cell Placement Using Meta-Genetic Parameter Optimization

A Genetic Approach to Standard Cell Placement Using Meta-Genetic Parameter Optimization. Khusro Shahookar Pinaka Mazumder. Genetic Algorithms. Genetic Placement. Population consists of an array of unordered triples x-position, y-position, cell number Fitness = 1 / (total wire length)

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A Genetic Approach to Standard Cell Placement Using Meta-Genetic Parameter Optimization

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  1. A Genetic Approach to Standard Cell Placement Using Meta-Genetic Parameter Optimization Khusro Shahookar Pinaka Mazumder

  2. Genetic Algorithms

  3. Genetic Placement • Population consists of an array of unordered triples • x-position, y-position, cell number • Fitness = 1 / (total wire length) • Cyclic crossover breeding scheme • Mutation: • Cell Swapping • Inversion

  4. Cyclic Crossover • Each cell’s position in offspring matches position in one of the offspring’s parents • Naturally cyclic (see example) • Randomly select starting parent and starting position • Give cells to offspring such that the above holds

  5. Cyclic Crossover - Example

  6. Cyclic Crossover - Example

  7. Cyclic Crossover - Example

  8. Cyclic Crossover - Example

  9. Cyclic Crossover - Example

  10. Cyclic Crossover - Example

  11. Cyclic Crossover - Example

  12. Cyclic Crossover - Example

  13. Cyclic Crossover - Example

  14. Cyclic Crossover - Example

  15. Mutation • Cell Swapping • Swap two cells at random • Changes placement • Inversion • Change the order of cells in the array • Does not change placement • Changes crossover behavior • “Genes” combine in new ways

  16. Meta-Genetic Parameter Opt. 1 • Recall Genetic Algorithm Takes Parameters: • Rc – Rate of Crossover • Ri – Rate of Inversion • Rm – Rate of Mutation (cell swapping) • Where do these parameters come from? • “Vanilla” genetic: The user • MGPO: Another genetic process

  17. Meta-Genetic Parameter Opt. 2 • A genetic algorithm inside a genetic algorithm • MetaGenetic() keeps a population of triples: • Rc, Ri, Rm • MetaGenetic() runs Genetic() once for each member of its population • MG() genetically improves parameters

  18. Demonstrations (time permitting) • Genetic Placement genetic –i gen_10_1.txt –np 1 –ng 1 –npg 10 –ngg 5 –v 7 • Meta-Genetic Optimization genetic –i gen_10_1.txt –np 3 –ng 5 –v 4 • Large-Scale Interaction (MG+G) genetic –i gen_50_1.txt –v 4

  19. Results versus Timberwolf

  20. Questions ?

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