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Hardware-aware probabilistic machine...

Galindez Olascoaga, Laura Isabel.

Hardware-aware probabilistic machine learning modelslearning, inference and use cases /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Hardware-aware probabilistic machine learning modelsby Laura Isabel Galindez Olascoaga, Wannes Meert, Marian Verhelst.
Reminder of title:	learning, inference and use cases /
Author:	Galindez Olascoaga, Laura Isabel.
other author:	Meert, Wannes.
Published:	Cham :Springer International Publishing :2021.
Description:	xii, 163 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Machine learning.
Online resource:	https://doi.org/10.1007/978-3-030-74042-9
ISBN:	9783030740429$q(electronic bk.)

Hardware-aware probabilistic machine learning modelslearning, inference and use cases /
Galindez Olascoaga, Laura Isabel.

Hardware-aware probabilistic machine learning modelslearning, inference and use cases /[electronic resource] :by Laura Isabel Galindez Olascoaga, Wannes Meert, Marian Verhelst. - Cham :Springer International Publishing :2021. - xii, 163 p. :ill., digital ;24 cm.

Introduction -- Background -- Hardware-Aware Cost Models -- Hardware-Aware Bayesian Networks for Sensor Front-End Quality Scaling -- Hardware-Aware Probabilistic Circuits -- Run-Time Strategies -- Conclusions.

This 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.

ISBN: 9783030740429$q(electronic bk.)

Standard No.: 10.1007/978-3-030-74042-9doiSubjects--Topical Terms:

188639
Machine learning.

LC Class. No.: Q325.5 / .G35 2021

Dewey Class. No.: 006.31

Hardware-aware probabilistic machine learning modelslearning, inference and use cases /
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505 0 $a Introduction -- Background -- Hardware-Aware Cost Models -- Hardware-Aware Bayesian Networks for Sensor Front-End Quality Scaling -- Hardware-Aware Probabilistic Circuits -- Run-Time Strategies -- Conclusions.
520 $a This 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.
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