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001 978-3-030-74042-9
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008 210519s2021 sz | s |||| 0|eng d
020 _a9783030740429
_9978-3-030-74042-9
024 7 _a10.1007/978-3-030-74042-9
_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 _aGalindez Olascoaga, Laura Isabel.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_950096
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.
300 _aXII, 163 p. 51 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
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
650 0 _aInternet of things.
_94027
650 0 _aCooperating objects (Computer systems).
_96195
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
700 1 _aVerhelst, Marian.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_950099
710 2 _aSpringerLink (Online service)
_950100
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