{"title":"Pareto Optimization of Analog circuits using Reinforcement Learning","authors":"Karthik Somayaji Ns, Peng Li","doi":"10.1145/3640463","DOIUrl":null,"url":null,"abstract":"<p>Analog circuit optimization and design presents a unique set of challenges in the IC design process. Many applications require for the designer to optimize for multiple competing objectives which poses a crucial challenge. Motivated by these practical aspects, we propose a novel method to tackle multi-objective optimization for analog circuit design in continuous action spaces. In particular, we propose to: (i) Extrapolate current techniques in Multi-Objective Reinforcement Learning (MORL) to continuous state and action spaces. (ii) Provide for a dynamically tunable trained model to query user defined preferences in multi-objective optimization in the analog circuit design context.</p>","PeriodicalId":50944,"journal":{"name":"ACM Transactions on Design Automation of Electronic Systems","volume":null,"pages":null},"PeriodicalIF":2.2000,"publicationDate":"2024-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACM Transactions on Design Automation of Electronic Systems","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1145/3640463","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, HARDWARE & ARCHITECTURE","Score":null,"Total":0}
引用次数: 0
Abstract
Analog circuit optimization and design presents a unique set of challenges in the IC design process. Many applications require for the designer to optimize for multiple competing objectives which poses a crucial challenge. Motivated by these practical aspects, we propose a novel method to tackle multi-objective optimization for analog circuit design in continuous action spaces. In particular, we propose to: (i) Extrapolate current techniques in Multi-Objective Reinforcement Learning (MORL) to continuous state and action spaces. (ii) Provide for a dynamically tunable trained model to query user defined preferences in multi-objective optimization in the analog circuit design context.
期刊介绍:
TODAES is a premier ACM journal in design and automation of electronic systems. It publishes innovative work documenting significant research and development advances on the specification, design, analysis, simulation, testing, and evaluation of electronic systems, emphasizing a computer science/engineering orientation. Both theoretical analysis and practical solutions are welcome.