# Genetic Approach to Standard Cell Placement (GASP) - PowerPoint PPT Presentation

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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)

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

• Fitness of a solution is 1/cost

• Tries to mimic biological evolution

### 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

• 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?

• 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

• Compared to TW ~ similar run time

• Same cost function

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

• GASP IS impressive!