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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

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!
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