{"title":"Test scheduling for core-based systems","authors":"K. Chakrabarty","doi":"10.1109/ICCAD.1999.810681","DOIUrl":null,"url":null,"abstract":"We present optimal solutions to the test scheduling problem for core-based systems. We show that test scheduling is equivalent to the m-processor open-shop scheduling problem and is therefore NP-complete. However a commonly-encountered instance of this problem (m=2) can be solved in polynomial time. For the general case (m>2), we present a mixed-integer linear programming (MILP) model for optimal scheduling and apply it to a representative core-based system using an MILP solver. We also extend the MILP model to allow optimal test set selection from a set of alternatives. Finally we present an efficient heuristic algorithm for handling larger systems for which the MILP model may be infeasible.","PeriodicalId":6414,"journal":{"name":"1999 IEEE/ACM International Conference on Computer-Aided Design. Digest of Technical Papers (Cat. No.99CH37051)","volume":"26 1","pages":"391-394"},"PeriodicalIF":0.0000,"publicationDate":"1999-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"59","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"1999 IEEE/ACM International Conference on Computer-Aided Design. Digest of Technical Papers (Cat. No.99CH37051)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCAD.1999.810681","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 59
Abstract
We present optimal solutions to the test scheduling problem for core-based systems. We show that test scheduling is equivalent to the m-processor open-shop scheduling problem and is therefore NP-complete. However a commonly-encountered instance of this problem (m=2) can be solved in polynomial time. For the general case (m>2), we present a mixed-integer linear programming (MILP) model for optimal scheduling and apply it to a representative core-based system using an MILP solver. We also extend the MILP model to allow optimal test set selection from a set of alternatives. Finally we present an efficient heuristic algorithm for handling larger systems for which the MILP model may be infeasible.