1 / 8

Genetic Approach to Standard Cell Placement (GASP)

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

trilby
Télécharger la présentation

Genetic Approach to Standard Cell Placement (GASP)

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Genetic Approach to Standard Cell Placement (GASP) Using Meta-Genetic Parameter Optimization

  2. 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

  3. 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.

  4. 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.

  5. 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

  6. 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!

  7. Questions?

  8. Thanks for Listening.You may now applaud!

More Related