{"title":"Data path synthesis for easy testability","authors":"M. Dhodhi, I. Ahmad, A. Ismaeel","doi":"10.1109/ATS.1994.367212","DOIUrl":null,"url":null,"abstract":"Synthesizing digital circuits which can be easily tested is an important and necessary aspect of a useful behavioral synthesis system. Testability at behavioral level can be enhanced by minimizing the number of self-adjacent registers (self-loops). This paper describes a technique for synthesizing an easy testable (loop-free) data path structure from a behavioral description of a design. The synthesis process uses an approach based on a problem-space genetic algorithm (PSGA) to perform concurrent scheduling and allocation of testable functional units to eliminate the self-loops. Experiments on benchmarks show that the self-loops can be eliminated with a minimum additional hardware resources to result in a testable data path.<<ETX>>","PeriodicalId":182440,"journal":{"name":"Proceedings of IEEE 3rd Asian Test Symposium (ATS)","volume":"76 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1994-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of IEEE 3rd Asian Test Symposium (ATS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ATS.1994.367212","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4
Abstract
Synthesizing digital circuits which can be easily tested is an important and necessary aspect of a useful behavioral synthesis system. Testability at behavioral level can be enhanced by minimizing the number of self-adjacent registers (self-loops). This paper describes a technique for synthesizing an easy testable (loop-free) data path structure from a behavioral description of a design. The synthesis process uses an approach based on a problem-space genetic algorithm (PSGA) to perform concurrent scheduling and allocation of testable functional units to eliminate the self-loops. Experiments on benchmarks show that the self-loops can be eliminated with a minimum additional hardware resources to result in a testable data path.<>