LEADER 03807nam 2200493 450 001 9910484854803321 005 20220122111147.0 010 $a3-030-74042-0 024 7 $a10.1007/978-3-030-74042-9 035 $a(CKB)4100000011938230 035 $a(DE-He213)978-3-030-74042-9 035 $a(MiAaPQ)EBC6628109 035 $a(Au-PeEL)EBL6628109 035 $a(OCoLC)1252700130 035 $a(PPN)25588835X 035 $a(EXLCZ)994100000011938230 100 $a20220122d2021 uy 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aHardware-aware probabilistic machine learning models $elearning, inference and use cases /$fLaura Isabel Galindez Olascoaga, Wannes Meetr, Marian Verhelst 205 $a1st ed. 2021. 210 1$aCham, Switzerland :$cSpringer,$d[2021] 210 4$dİ2021 215 $a1 online resource (XII, 163 p. 51 illus.) 311 $a3-030-74041-2 320 $aIncludes bibliographical references and index. 327 $aIntroduction -- Background -- Hardware-Aware Cost Models -- Hardware-Aware Bayesian Networks for Sensor Front-End Quality Scaling -- Hardware-Aware Probabilistic Circuits -- Run-Time Strategies -- Conclusions. 330 $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. . 606 $aMachine learning 615 0$aMachine learning. 676 $a006.31 700 $aGalindez Olascoaga$b Laura Isabel$0981966 702 $aMeetr$b Wannes 702 $aVerhelst$b Marian 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910484854803321 996 $aHardware-Aware Probabilistic Machine Learning Models$92241111 997 $aUNINA