Hardware-aware probabilistic machine learning models : learning, inference and use cases / Laura Isabel Galindez Olascoaga, Wannes Meetr, Marian Verhelst.
2021
Q325.5 .G35 2021
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Title
Hardware-aware probabilistic machine learning models : learning, inference and use cases / Laura Isabel Galindez Olascoaga, Wannes Meetr, Marian Verhelst.
ISBN
9783030740429 (electronic bk.)
3030740420 (electronic bk.)
9783030740436 (print)
3030740439
9783030740443 (print)
3030740447
9783030740412
3030740412
3030740420 (electronic bk.)
9783030740436 (print)
3030740439
9783030740443 (print)
3030740447
9783030740412
3030740412
Published
Cham, Switzerland : Springer, [2021]
Language
English
Description
1 online resource : illustrations
Item Number
10.1007/978-3-030-74042-9 doi
Call Number
Q325.5 .G35 2021
Dewey Decimal Classification
006.31
Summary
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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Includes bibliographical references and index.
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Online resource; title from digital title page (viewed on June 14, 2021).
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Table of Contents
Introduction
Background
Hardware-Aware Cost Models
Hardware-Aware Bayesian Networks for Sensor Front-End Quality Scaling
Hardware-Aware Probabilistic Circuits
Run-Time Strategies
Conclusions.
Background
Hardware-Aware Cost Models
Hardware-Aware Bayesian Networks for Sensor Front-End Quality Scaling
Hardware-Aware Probabilistic Circuits
Run-Time Strategies
Conclusions.