{"title":"基于自动sat的片上位翻转检测器设计方法","authors":"Pouya Taatizadeh, N. Nicolici","doi":"10.1109/ICCAD.2017.8203766","DOIUrl":null,"url":null,"abstract":"Hardware invariants are known to facilitate bit-flip detection during post-silicon validation. In this paper, we present a fully automated SAT-based methodology for fast generation of hardware invariants by using the built-in pruning mechanisms within SAT solvers, namely learned clauses. These candidates are evaluated for their potential to detect bit-flips using a new incremental SAT-based approach. In addition to speeding-up the simulation-based approaches for invariant generation and evaluation, when compared to the known art, our results show improvements in both the number of flip-flops that can be covered for bit-flip detection, as well as for the on-chip area for the bit-flip detection unit.","PeriodicalId":126686,"journal":{"name":"2017 IEEE/ACM International Conference on Computer-Aided Design (ICCAD)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"An automated SAT-based method for the design of on-chip bit-flip detectors\",\"authors\":\"Pouya Taatizadeh, N. Nicolici\",\"doi\":\"10.1109/ICCAD.2017.8203766\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Hardware invariants are known to facilitate bit-flip detection during post-silicon validation. In this paper, we present a fully automated SAT-based methodology for fast generation of hardware invariants by using the built-in pruning mechanisms within SAT solvers, namely learned clauses. These candidates are evaluated for their potential to detect bit-flips using a new incremental SAT-based approach. In addition to speeding-up the simulation-based approaches for invariant generation and evaluation, when compared to the known art, our results show improvements in both the number of flip-flops that can be covered for bit-flip detection, as well as for the on-chip area for the bit-flip detection unit.\",\"PeriodicalId\":126686,\"journal\":{\"name\":\"2017 IEEE/ACM International Conference on Computer-Aided Design (ICCAD)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-11-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 IEEE/ACM International Conference on Computer-Aided Design (ICCAD)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICCAD.2017.8203766\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 IEEE/ACM International Conference on Computer-Aided Design (ICCAD)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCAD.2017.8203766","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
An automated SAT-based method for the design of on-chip bit-flip detectors
Hardware invariants are known to facilitate bit-flip detection during post-silicon validation. In this paper, we present a fully automated SAT-based methodology for fast generation of hardware invariants by using the built-in pruning mechanisms within SAT solvers, namely learned clauses. These candidates are evaluated for their potential to detect bit-flips using a new incremental SAT-based approach. In addition to speeding-up the simulation-based approaches for invariant generation and evaluation, when compared to the known art, our results show improvements in both the number of flip-flops that can be covered for bit-flip detection, as well as for the on-chip area for the bit-flip detection unit.