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Compact and fast machine learning ac...

Huang, Hantao.

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Compact and fast machine learning accelerator for IoT devices

Record Type:	Electronic resources : Monograph/item
Title/Author:	Compact and fast machine learning accelerator for IoT devices/ by Hantao Huang, Hao Yu.
Author:	Huang, Hantao.
other author:	Yu, Hao.
Published:	Singapore :Springer Singapore : : 2019.,
Description:	ix, 149 p. :ill., digital ;24 cm.
[NT 15003449]:	Computing on Edge Devices in Internet-of-things (IoT) -- The Rise of Machine Learning in IoT system -- Least-squares-solver for Shadow Neural Network -- Tensor-solver for Deep Neural Network -- Distributed-solver for Networked Neural Network -- Conclusion.
Contained By:	Springer eBooks
Subject:	Machine learning. -
Online resource:	https://doi.org/10.1007/978-981-13-3323-1
ISBN:	9789811333231

Compact and fast machine learning accelerator for IoT devices
Huang, Hantao.

Compact and fast machine learning accelerator for IoT devices[electronic resource] /by Hantao Huang, Hao Yu. - Singapore :Springer Singapore :2019. - ix, 149 p. :ill., digital ;24 cm. - Computer architecture and design methodologies,2367-3478. - Computer architecture and design methodologies..

Computing on Edge Devices in Internet-of-things (IoT) -- The Rise of Machine Learning in IoT system -- Least-squares-solver for Shadow Neural Network -- Tensor-solver for Deep Neural Network -- Distributed-solver for Networked Neural Network -- Conclusion.

This book presents the latest techniques for machine learning based data analytics on IoT edge devices. A comprehensive literature review on neural network compression and machine learning accelerator is presented from both algorithm level optimization and hardware architecture optimization. Coverage focuses on shallow and deep neural network with real applications on smart buildings. The authors also discuss hardware architecture design with coverage focusing on both CMOS based computing systems and the new emerging Resistive Random-Access Memory (RRAM) based systems. Detailed case studies such as indoor positioning, energy management and intrusion detection are also presented for smart buildings.

ISBN: 9789811333231

Standard No.: 10.1007/978-981-13-3323-1doiSubjects--Topical Terms:

533906
Machine learning.

LC Class. No.: Q325.5 / .H836 2019

Dewey Class. No.: 006.31

Compact and fast machine learning accelerator for IoT devices
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300 $a ix, 149 p. : $b ill., digital ; $c 24 cm.
490 1 $a Computer architecture and design methodologies, $x 2367-3478
505 0 $a Computing on Edge Devices in Internet-of-things (IoT) -- The Rise of Machine Learning in IoT system -- Least-squares-solver for Shadow Neural Network -- Tensor-solver for Deep Neural Network -- Distributed-solver for Networked Neural Network -- Conclusion.
520 $a This book presents the latest techniques for machine learning based data analytics on IoT edge devices. A comprehensive literature review on neural network compression and machine learning accelerator is presented from both algorithm level optimization and hardware architecture optimization. Coverage focuses on shallow and deep neural network with real applications on smart buildings. The authors also discuss hardware architecture design with coverage focusing on both CMOS based computing systems and the new emerging Resistive Random-Access Memory (RRAM) based systems. Detailed case studies such as indoor positioning, energy management and intrusion detection are also presented for smart buildings.
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650 0 $a Internet of things. $3 2057703
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650 2 4 $a Optimization. $3 891104
700 1 $a Yu, Hao. $3 1569152
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950 $a Intelligent Technologies and Robotics (Springer-42732)