LEADER 03611oam 2200517 450 001 9910427674703321 005 20210416192904.0 010 $a3-030-55704-9 024 7 $a10.1007/978-3-030-55704-1 035 $a(CKB)4100000011558791 035 $a(DE-He213)978-3-030-55704-1 035 $a(MiAaPQ)EBC6383517 035 $a(PPN)252509943 035 $a(EXLCZ)994100000011558791 100 $a20210416d2020 uy 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aSoft error reliability using virtual platforms $eearly evaluation of multicore systems /$fFelipe Rocha da Rosa, Luciano Ost and Ricardo Reis 205 $a1st ed. 2020. 210 1$aCham, Switzerland :$cSpringer,$d[2020] 210 4$d©2020 215 $a1 online resource (XI, 136 p. 53 illus., 51 illus. in color.) 311 $a3-030-55703-0 320 $aIncludes bibliographical references and index. 327 $aChapter 1 . Introduction -- Chapter 2. Background on Soft Errors -- Chapter 3. Fault Injection Framework Using Virtual Platforms -- Chapter 4. Performance and Accuracy Assessment of Fault Injection Frameworks Based on VPs -- Chapter 5. Extensive Soft Error Evaluation -- Chapter 6. Machine Learning Applied to Soft Error Assessment in Multicoresystems. 330 $aThis book describes the benefits and drawbacks inherent in the use of virtual platforms (VPs) to perform fast and early soft error assessment of multicore systems. The authors show that VPs provide engineers with appropriate means to investigate new and more efficient fault injection and mitigation techniques. Coverage also includes the use of machine learning techniques (e.g., linear regression) to speed-up the soft error evaluation process by pinpointing parameters (e.g., architectural) with the most substantial impact on the software stack dependability. This book provides valuable information and insight through more than 3 million individual scenarios and 2 million simulation-hours. Further, this book explores machine learning techniques usage to navigate large fault injection datasets. Describes the most suitable and efficient virtual platforms to include fault injection capabilities, aiming to support the soft error analysis of state-of-the-art processor models; Includes analysis and port of several benchmarks from embedded and HPC domains, including the Rodinia and NASA NAS Parallel Benchmark (NPB) suites; Introduces four novel, non-intrusive FI techniques enabling software engineers to perform in-depth and relevant soft error evaluation, addressing the gap between the available FI tools and the industry requirements; Explores machine learning techniques that can be used to enable the identification of individual (or combinations of) microarchitectural and software parameters that present the most substantial relation relationship with each detected soft error or failure. 606 $aProcessor Architectures 606 $aElectronics and Microelectronics, Instrumentation 606 $aCircuits and Systems 615 0$aProcessor Architectures. 615 0$aElectronics and Microelectronics, Instrumentation. 615 0$aCircuits and Systems. 676 $a621.3815 700 $aRocha da Rosa$b Felipe$0976008 702 $aReis$b Ricardo 702 $aOst$b Luciano 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bUtOrBLW 906 $aBOOK 912 $a9910427674703321 996 $aSoft error reliability using virtual platforms$92222513 997 $aUNINA