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020			_a9783030359713
024	7		_a10.1007/978-3-030-35971-3 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
041	0		_aeng
050		4	_aQ325.5 _b2020 EB
100	1		_aKang, Mingu _eautor _9672537
245	1	0	_aDeep In-memory Architectures for Machine Learning _cby Mingu Kang, Sujan Gonugondla, Naresh R. Shanbhag
250			_aFirst edition
264		1	_aCham _bSpringer International Publishing : _bImprint Springer _c2020.
300			_a1 recurso en línea (X, 174 páginas) _b104 ilustraciones, 65 ilustraciones a color
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_atext file _bPDF
505	0		_aIntroduction -- The Deep In-memory Architecture (DIMA) -- DIMA Prototype Integrated Circuits -- A Variation-Tolerant DIMA via On-Chip Training -- Mapping Inference Algorithms to DIMA -- PROMISE: A DIMA-based Accelerator -- Future Prospects -- Index.
520	3		_aThis book describes the recent innovation of deep in-memory architectures for realizing AI systems that operate at the edge of energy-latency-accuracy trade-offs. From first principles to lab prototypes, this book provides a comprehensive view of this emerging topic for both the practicing engineer in industry and the researcher in academia. The book is a journey into the exciting world of AI systems in hardware. Describes deep in-memory architectures for AI systems from first principles, covering both circuit design and architectures; Discusses how DIMAs pushes the limits of energy-delay product of decision-making machines via its intrinsic energy-SNR trade-off; Offers readers a unique Shannon-inspired perspective to understand the system-level energy-accuracy trade-off and robustness in such architectures; Illustrates principles and design methods via case studies of actual integrated circuit prototypes with measured results in the laboratory; Presents DIMA's various models to evaluate DIMA's decision-making accuracy, energy, and latency trade-offs with various design parameter.
988			_aPrimersemestre_2020_Engineering
650		7	_2embne _aAprendizaje automático _9166090
700	1		_aGonugondla, Sujan. _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut
700	1		_aShanbhag, Naresh R. _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9783030359706
776	0	8	_iPrinted edition: _z9783030359720
776	0	8	_iPrinted edition: _z9783030359737
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-35971-3 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE _n0
998			_b03/2020 _dz _eb _zSI