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Embedded machine learning for cyber-physical, IOT, and edge computing = software optimizations and hardware/software codesign /

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:	software optimizations and hardware/software codesign /
other author:	Pasricha, Sudeep.
Published:	Cham :Springer Nature Switzerland : : 2024.,
Description:	xiv, 477 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Cooperating objects (Computer systems) -
Online resource:	https://doi.org/10.1007/978-3-031-39932-9
ISBN:	9783031399329

Embedded machine learning for cyber-physical, IOT, and edge computing = software optimizations and hardware/software codesign /
Embedded machine learning for cyber-physical, IOT, and edge computingsoftware optimizations and hardware/software codesign /[electronic resource] :edited by Sudeep Pasricha, Muhammad Shafique. - Cham :Springer Nature Switzerland :2024. - xiv, 477 p. :ill. (some col.), digital ;24 cm.

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: 9783031399329

Standard No.: 10.1007/978-3-031-39932-9doiSubjects--Topical Terms:

2055414
Cooperating objects (Computer systems)

LC Class. No.: TK7895.E42

Dewey Class. No.: 006.22

Embedded machine learning for cyber-physical, IOT, and edge computing = software optimizations and hardware/software codesign /
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245 0 0 $a Embedded machine learning for cyber-physical, IOT, and edge computing $h [electronic resource] : $b software optimizations and hardware/software codesign / $c edited by Sudeep Pasricha, Muhammad Shafique.
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300 $a xiv, 477 p. : $b ill. (some col.), digital ; $c 24 cm.
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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