Genetic approach to standard cell placement gasp
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Genetic Approach to Standard Cell Placement (GASP). Using Meta-Genetic Parameter Optimization. What is a Genetic Algorithm?. Maintains a pool of solutions called the population Generates new solutions by combining genes from two parents. Parents are selected according to fitness

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Genetic Approach to Standard Cell Placement (GASP)

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Genetic approach to standard cell placement gasp

Genetic Approach to Standard Cell Placement (GASP)

Using Meta-Genetic Parameter Optimization


What is a genetic algorithm

What is a Genetic Algorithm?

  • Maintains a pool of solutions called the population

  • Generates new solutions by combining genes from two parents. Parents are selected according to fitness

  • Fitness of a solution is 1/cost

  • Tries to mimic biological evolution


Important terminology

Important Terminology

  • Crossover & Crossover rate (PMX) – Crossover is the operation that generates new solutions from parents.

  • Mutation & Mutation rate – Mutation introduces new genes or tries lost genes in a new context

  • Inversion & Inversion rate – Only Changes Representation of Solution.


Implementation details

Implementation Details

  • Data Structures: Each solution is 3 arrays, connectivity list, span structures

  • Crossover: PMX

  • Cost function: Semi-perimeter

  • Population is sorted according to cost (not fitness!)– Many benefits to sorting!

  • Np = 24, RC = .34, RM=.005, RI = .15, weighted random parent selection.


What is meta genetic parameter optimization

What is meta-genetic parameter optimization?

  • We want to find the best crossover rate, mutation rate, population size, inversion rate, parent selection technique, etc

  • Run a genetic algorithm on the GASP algorithm to find the best parameters.

  • Best results: NP= 24, RC= .33, RI= .15, RM = .005, Crossover = Cycle


Performance of gasp

Performance of GASP

  • Compared to TW ~ similar run time

  • Same cost function

  • GASP returns results which are 5%-20% better than TimberWolf

  • GASP IS impressive!


Questions

Questions?


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