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Embedded machine learning for cyber-physical, iot, and edge computing = hardware architectures /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Embedded machine learning for cyber-physical, iot, and edge computing/ edited by Sudeep Pasricha, Muhammad Shafique.
Reminder of title:	hardware architectures /
other author:	Pasricha, Sudeep.
Published:	Cham :Springer International Publishing : : 2024.,
Description:	xiv, 412 p. :ill. (some col.), digital ;24 cm.
[NT 15003449]:	Introduction -- Efficient Hardware Acceleration for Embedded Machine Learning -- Memory Design and Optimization for Embedded Machine Learning -- Efficient Software Design of Embedded Machine Learning -- Hardware-Software Co-Design for Embedded Machine Learning -- Emerging Technologies for Embedded Machine Learning -- Mobile, IoT, and Edge Application Use-Cases for Embedded Machine Learning -- Cyber-Physical Application Use-Cases for Embedded Machine Learning.
Contained By:	Springer Nature eBook
Subject:	Cooperating objects (Computer systems) -
Online resource:	https://doi.org/10.1007/978-3-031-19568-6
ISBN:	9783031195686

Embedded machine learning for cyber-physical, iot, and edge computing = hardware architectures /
Embedded machine learning for cyber-physical, iot, and edge computinghardware architectures /[electronic resource] :edited by Sudeep Pasricha, Muhammad Shafique. - Cham :Springer International Publishing :2024. - xiv, 412 p. :ill. (some col.), digital ;24 cm.

Introduction -- Efficient Hardware Acceleration for Embedded Machine Learning -- Memory Design and Optimization for Embedded Machine Learning -- Efficient Software Design of Embedded Machine Learning -- Hardware-Software Co-Design for Embedded Machine Learning -- Emerging Technologies for Embedded Machine Learning -- Mobile, IoT, and Edge Application Use-Cases for Embedded Machine Learning -- Cyber-Physical Application Use-Cases for Embedded Machine Learning.

This book presents recent advances towards the goal of enabling efficient implementation of machine learning models on resource-constrained systems, covering different application domains. The focus is on presenting interesting and new use cases of applying machine learning to innovative application domains, exploring the efficient hardware design of efficient machine learning accelerators, memory optimization techniques, illustrating model compression and neural architecture search techniques for energy-efficient and fast execution on resource-constrained hardware platforms, and understanding hardware-software codesign techniques for achieving even greater energy, reliability, and performance benefits. Discusses efficient implementation of machine learning in embedded, CPS, IoT, and edge computing; Offers comprehensive coverage of hardware design, software design, and hardware/software co-design and co-optimization; Describes real applications to demonstrate how embedded, CPS, IoT, and edge applications benefit from machine learning.

ISBN: 9783031195686

Standard No.: 10.1007/978-3-031-19568-6doiSubjects--Topical Terms:

2055414
Cooperating objects (Computer systems)

LC Class. No.: TK7895.E42 / E43 2024

Dewey Class. No.: 006.22

Embedded machine learning for cyber-physical, iot, and edge computing = hardware architectures /
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245 0 0 $a Embedded machine learning for cyber-physical, iot, and edge computing $h [electronic resource] : $b hardware architectures / $c edited by Sudeep Pasricha, Muhammad Shafique.
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300 $a xiv, 412 p. : $b ill. (some col.), digital ; $c 24 cm.
505 0 $a Introduction -- Efficient Hardware Acceleration for Embedded Machine Learning -- Memory Design and Optimization for Embedded Machine Learning -- Efficient Software Design of Embedded Machine Learning -- Hardware-Software Co-Design for Embedded Machine Learning -- Emerging Technologies for Embedded Machine Learning -- Mobile, IoT, and Edge Application Use-Cases for Embedded Machine Learning -- Cyber-Physical Application Use-Cases for Embedded Machine Learning.
520 $a This book presents recent advances towards the goal of enabling efficient implementation of machine learning models on resource-constrained systems, covering different application domains. The focus is on presenting interesting and new use cases of applying machine learning to innovative application domains, exploring the efficient hardware design of efficient machine learning accelerators, memory optimization techniques, illustrating model compression and neural architecture search techniques for energy-efficient and fast execution on resource-constrained hardware platforms, and understanding hardware-software codesign techniques for achieving even greater energy, reliability, and performance benefits. Discusses efficient implementation of machine learning in embedded, CPS, IoT, and edge computing; Offers comprehensive coverage of hardware design, software design, and hardware/software co-design and co-optimization; Describes real applications to demonstrate how embedded, CPS, IoT, and edge applications benefit from machine learning.
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650 0 $a Machine learning. $3 533906
650 1 4 $a Embedded Systems. $3 3592715
650 2 4 $a Cyber-Physical Systems. $3 3591993
650 2 4 $a Artificial Intelligence. $3 769149
700 1 $a Pasricha, Sudeep. $3 1000595
700 1 $a Shafique, Muhammad. $3 2191490
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856 4 0 $u https://doi.org/10.1007/978-3-031-19568-6
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