Raviv Gal, E. Haber, Brian Irwin, Marwa Mouallem, Bilal Saleh, A. Ziv
{"title":"Using Deep Neural Networks And Derivative Free Optimization To Accelerate Coverage Closure","authors":"Raviv Gal, E. Haber, Brian Irwin, Marwa Mouallem, Bilal Saleh, A. Ziv","doi":"10.1109/MLCAD52597.2021.9531234","DOIUrl":null,"url":null,"abstract":"In computer aided design (CAD), a core task is to optimize the parameters of noisy simulations. Derivative free optimization (DFO) methods are the most common choice for this task. In this paper, we show how four DFO methods, specifically implicit filtering (IF), simulated annealing (SA), genetic algorithms (GA), and particle swarm (PS), can be accelerated using a deep neural network (DNN) that acts as a surrogate model of the objective function. In particular, we demonstrate the applicability of the DNN accelerated DFO approach to the coverage directed generation (CDG) problem that is commonly solved by hardware verification teams.","PeriodicalId":210763,"journal":{"name":"2021 ACM/IEEE 3rd Workshop on Machine Learning for CAD (MLCAD)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2021-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 ACM/IEEE 3rd Workshop on Machine Learning for CAD (MLCAD)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MLCAD52597.2021.9531234","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
In computer aided design (CAD), a core task is to optimize the parameters of noisy simulations. Derivative free optimization (DFO) methods are the most common choice for this task. In this paper, we show how four DFO methods, specifically implicit filtering (IF), simulated annealing (SA), genetic algorithms (GA), and particle swarm (PS), can be accelerated using a deep neural network (DNN) that acts as a surrogate model of the objective function. In particular, we demonstrate the applicability of the DNN accelerated DFO approach to the coverage directed generation (CDG) problem that is commonly solved by hardware verification teams.