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000946102 0247_ $$a10.1007/978-3-030-55704-1$$2doi
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000946102 08204 $$a621.3815$$223
000946102 1001_ $$aRocha da Rosa, Felipe$$eauthor.
000946102 24510 $$aSoft error reliability using virtual platforms :$$bearly evaluation of multicore systems /$$cFelipe Rocha da Rosa, Luciano Ost, Ricardo Reis.
000946102 264_1 $$aCham, Switzerland :$$bSpringer,$$c[2020]
000946102 300__ $$a1 online resource (142 p.)
000946102 336__ $$atext$$btxt$$2rdacontent
000946102 337__ $$acomputer$$bc$$2rdamedia
000946102 338__ $$aonline resource$$bcr$$2rdacarrier
000946102 347__ $$bPDF$$2rda
000946102 347__ $$atext file$$2rdaft$$0http://rdaregistry.info/termList/fileType/1002
000946102 5050_ $$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.
000946102 506__ $$aAccess limited to authorized users.
000946102 520__ $$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.
000946102 588__ $$aDescription based on print version record.
000946102 650_0 $$aElectronic circuits.
000946102 650_0 $$aElectronics.
000946102 650_0 $$aMicroelectronics.
000946102 650_0 $$aMicroprocessors.
000946102 7001_ $$aOst, Luciano.
000946102 7001_ $$aReis, Ricardo.
000946102 77608 $$iPrint version:$$aRocha da Rosa, Felipe$$tSoft Error Reliability Using Virtual Platforms : Early Evaluation of Multicore Systems$$dCham : Springer International Publishing AG,c2020$$z9783030557034
000946102 852__ $$bebk
000946102 85640 $$3SpringerLink$$uhttps://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-3-030-55704-1$$zOnline Access$$91397441.1
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000946102 980__ $$aEBOOK
000946102 980__ $$aBIB
000946102 982__ $$aEbook
000946102 983__ $$aOnline
000946102 994__ $$a92$$bISE