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Embedded Machine Learning for Cyber-Physical, IoT, and Edge Computing, 1st ed. 2024 Hardware Architectures

Langue : Anglais

Coordonnateurs : Pasricha Sudeep, Shafique Muhammad

Couverture de l’ouvrage Embedded Machine Learning for Cyber-Physical, IoT, and Edge Computing

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.

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.

Sudeep Pasricha is a Walter Scott Jr. College of Engineering Professor in the Department of Electrical and Computer Engineering, the Department of Computer Science, and the Department of Systems Engineering at Colorado State University. He is Director of the Embedded, High Performance, and Intelligent Computing (EPIC) Laboratory and the Chair of Computer Engineering. Prof. Pasricha received the B.E. degree in Electronics and Communication Engineering from Delhi Institute of Technology, India, and his Ph.D. in Computer Science from the University of California, Irvine. He has several years of work experience in industry where his work focused on electronic chip design automation, model based design, and embedded system codesign. Prof. Pasricha has received more than $7M in funding for his research from various sponsors including the NSF, SRC, AFOSR, ORNL, DoD, Fiat-Chrysler, and NASA. He has co-authored multiple books, contributed to several book chapters, and published more than 250 research articles in peer-reviewed conferences, journals, and books. He has been part of panels, keynotes, and also organized special sessions and tutorials on his research areas at premier conferences. He is a Senior Member of the IEEE (Computer Society), Distinguished Member of the ACM, and an ACM Distinguished Speaker.

Prof. Pasricha’s research broadly focuses on software algorithms, hardware architectures, and hardware-software co-design for energy-efficient, fault-tolerant, real-time, and secure computing. These efforts target multi-scale computing platforms, including embedded and Internet of Things (IoT) systems, cyber-physical systems, mobile devices, and datacenters. Prof. Pasricha has received 16 Best Paper Awards and Nominations at various IEEE and ACM conferences, including at DAC, ASPDAC, NOCS, GLSVLSI, SLIP, AICCSA, and ISQED. Other notable awards include: the 2019 George T. Abell Outstanding Research Faculty Award, the 2016-2018 University Distinguished Monfort Prof
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

Date de parution :

Ouvrage de 412 p.

15.5x23.5 cm

Disponible chez l'éditeur (délai d'approvisionnement : 15 jours).

116,04 €

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