000			04037nam a22005535i 4500
001			978-3-030-55704-1
003			DE-He213
005			20240423125048.0
007			cr nn 008mamaa
008			201102s2020 sz | s |||| 0|eng d
020			_a9783030557041 _9978-3-030-55704-1
024	7		_a10.1007/978-3-030-55704-1 _2doi
050		4	_aTK7867-7867.5
072		7	_aTJFC _2bicssc
072		7	_aTEC008010 _2bisacsh
072		7	_aTJFC _2thema
082	0	4	_a621.3815 _223
100	1		_aRocha da Rosa, Felipe. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut
245	1	0	_aSoft Error Reliability Using Virtual Platforms _h[electronic resource] : _bEarly Evaluation of Multicore Systems / _cby Felipe Rocha da Rosa, Luciano Ost, Ricardo Reis.
250			_a1st ed. 2020.
264		1	_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2020.
300			_aXI, 136 p. 53 illus., 51 illus. in color. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
505	0		_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.
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-artprocessor 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.
650		0	_aElectronic circuits.
650		0	_aElectronics.
650		0	_aMicroprocessors.
650		0	_aComputer architecture.
650	1	4	_aElectronic Circuits and Systems.
650	2	4	_aElectronics and Microelectronics, Instrumentation.
650	2	4	_aProcessor Architectures.
700	1		_aOst, Luciano. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut
700	1		_aReis, Ricardo. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut
710	2		_aSpringerLink (Online service)
773	0		_tSpringer Nature eBook
776	0	8	_iPrinted edition: _z9783030557034
776	0	8	_iPrinted edition: _z9783030557058
776	0	8	_iPrinted edition: _z9783030557065
856	4	0	_uhttps://doi.org/10.1007/978-3-030-55704-1
912			_aZDB-2-SCS
912			_aZDB-2-SXCS
942			_cSPRINGER
999			_c173802 _d173802