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008			220219s2021 sz | s |||| 0|eng d
020			_a9783030740429
024	7		_a10.1007/978-3-030-74042-9 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQ325.5 _b2021 EB
100	1		_aGalíndez Olascoaga, Laura Isabel _eautor _4 _4http://id.loc.gov/vocabulary/relators/aut _9682273
245	1	0	_aHardware-Aware Probabilistic Machine Learning Models : _bLearning, Inference and Use Cases _cby Laura Isabel Galindez Olascoaga, Wannes Meert, Marian Verhelst
250			_aFirst edition 2021
264		1	_aCham _bSpringer International Publishing _c2021
300			_a1 recurso en línea (XII, 163 páginas) _b 51 ilustraciones
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_aarchivo de texto _bPDF
490	0		_aEngineering (SpringerNature-11647)
490	0		_aEngineering (R0) (SpringerNature-43712)
505	0		_aIntroduction -- Background -- Hardware-Aware Cost Models -- Hardware-Aware Bayesian Networks for Sensor Front-End Quality Scaling -- Hardware-Aware Probabilistic Circuits -- Run-Time Strategies -- Conclusions.
520	3		_aThis book proposes probabilistic machine learning models that represent the hardware properties of the device hosting them. These models can be used to evaluate the impact that a specific device configuration may have on resource consumption and performance of the machine learning task, with the overarching goal of balancing the two optimally. The book first motivates extreme-edge computing in the context of the Internet of Things (IoT) paradigm. Then, it briefly reviews the steps involved in the execution of a machine learning task and identifies the implications associated with implementing this type of workload in resource-constrained devices. The core of this book focuses on augmenting and exploiting the properties of Bayesian Networks and Probabilistic Circuits in order to endow them with hardware-awareness. The proposed models can encode the properties of various device sub-systems that are typically not considered by other resource-aware strategies, bringing about resource-saving opportunities that traditional approaches fail to uncover. The performance of the proposed models and strategies is empirically evaluated for several use cases. All of the considered examples show the potential of attaining significant resource-saving opportunities with minimal accuracy losses at application time. Overall, this book constitutes a novel approach to hardware-algorithm co-optimization that further bridges the fields of Machine Learning and Electrical Engineering. Introduces a new, systematic approach for the realization of hardware-awareness with probabilistic models; Enables readers to accommodate various systems and applications, as demonstrated with multiple use cases targeting distinct types of devices; Describes novel methods to deal with some of the challenges of extreme-edge computing, a paradigm that has recently garnered attention as a complementary approach to cloud computing; Represents one of the first efforts systematically to bring probabilistic inference to the world of edge computing, by means of novel algorithmic insights and strategies. .
988			_aSpringer_Engineering_2021
650		7	_2embne _9166090 _aAprendizaje automático
700	1		_aMeert, Wannes _eautor _4 _4http://id.loc.gov/vocabulary/relators/aut _9682274
700	1		_aVerhelst, Marian _eautor _4 _4http://id.loc.gov/vocabulary/relators/aut _9682275
776	0	8	_iPrinted edition: _z9783030740412
776	0	8	_iPrinted edition: _z9783030740436
776	0	8	_iPrinted edition: _z9783030740443
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-74042-9 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b02/2022 _dz _eIG _zSI