000 | 04397nam a22005415i 4500 | ||
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001 | 978-3-030-74042-9 | ||
003 | DE-He213 | ||
005 | 20220801220411.0 | ||
007 | cr nn 008mamaa | ||
008 | 210519s2021 sz | s |||| 0|eng d | ||
020 |
_a9783030740429 _9978-3-030-74042-9 |
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024 | 7 |
_a10.1007/978-3-030-74042-9 _2doi |
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_a621.3815 _223 |
100 | 1 |
_aGalindez Olascoaga, Laura Isabel. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _950096 |
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245 | 1 | 0 |
_aHardware-Aware Probabilistic Machine Learning Models _h[electronic resource] : _bLearning, Inference and Use Cases / _cby Laura Isabel Galindez Olascoaga, Wannes Meert, Marian Verhelst. |
250 | _a1st ed. 2021. | ||
264 | 1 |
_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2021. |
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300 |
_aXII, 163 p. 51 illus. _bonline resource. |
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336 |
_atext _btxt _2rdacontent |
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337 |
_acomputer _bc _2rdamedia |
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338 |
_aonline resource _bcr _2rdacarrier |
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347 |
_atext file _bPDF _2rda |
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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 | _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. . | ||
650 | 0 |
_aElectronic circuits. _919581 |
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650 | 0 |
_aInternet of things. _94027 |
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650 | 0 |
_aCooperating objects (Computer systems). _96195 |
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650 | 1 | 4 |
_aElectronic Circuits and Systems. _950097 |
650 | 2 | 4 |
_aInternet of Things. _94027 |
650 | 2 | 4 |
_aCyber-Physical Systems. _932475 |
700 | 1 |
_aMeert, Wannes. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _950098 |
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700 | 1 |
_aVerhelst, Marian. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _950099 |
|
710 | 2 |
_aSpringerLink (Online service) _950100 |
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773 | 0 | _tSpringer Nature eBook | |
776 | 0 | 8 |
_iPrinted edition: _z9783030740412 |
776 | 0 | 8 |
_iPrinted edition: _z9783030740436 |
776 | 0 | 8 |
_iPrinted edition: _z9783030740443 |
856 | 4 | 0 | _uhttps://doi.org/10.1007/978-3-030-74042-9 |
912 | _aZDB-2-ENG | ||
912 | _aZDB-2-SXE | ||
942 | _cEBK | ||
999 |
_c78526 _d78526 |