東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁 到查詢結果 [ subject:"Processor Architectures." ]

切換: 標籤 | MARC模式 | ISBD

Compact and fast machine learning ac...

Huang, Hantao.

FindBook

Google Book

Amazon

博客來

Compact and fast machine learning accelerator for IoT devices

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Compact and fast machine learning accelerator for IoT devices/ by Hantao Huang, Hao Yu.
作者:	Huang, Hantao.
其他作者:	Yu, Hao.
出版者:	Singapore :Springer Singapore : : 2019.,
面頁冊數:	ix, 149 p. :ill., digital ;24 cm.
內容註:	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
標題:	Machine learning. -
電子資源:	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
LDR:02036nmm a2200337 a 4500 001 2178795
003 DE-He213
005 20190705144500.0
006 m d
007 cr nn 008maaau
008 191122s2019 si s 0 eng d
020 $a 9789811333231 $q (electronic bk.)
020 $a 9789811333224 $q (paper)
024 7 $a 10.1007/978-981-13-3323-1 $2 doi
035 $a 978-981-13-3323-1
040 $a GP $c GP
041 0 $a eng
050 4 $a Q325.5 $b .H836 2019
072 7 $a UYQ $2 bicssc
072 7 $a COM004000 $2 bisacsh
072 7 $a UYQ $2 thema
082 0 4 $a 006.31 $2 23
090 $a Q325.5 $b .H874 2019
100 1 $a Huang, Hantao. $3 3383316
245 1 0 $a Compact and fast machine learning accelerator for IoT devices $h [electronic resource] / $c by Hantao Huang, Hao Yu.
260 $a Singapore : $b Springer Singapore : $b Imprint: Springer, $c 2019.
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.
650 0 $a Machine learning. $3 533906
650 0 $a Internet of things. $3 2057703
650 1 4 $a Computational Intelligence. $3 1001631
650 2 4 $a Processor Architectures. $3 892680
650 2 4 $a Optimization. $3 891104
700 1 $a Yu, Hao. $3 1569152
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer eBooks
830 0 $a Computer architecture and design methodologies. $3 2203505
856 4 0 $u https://doi.org/10.1007/978-981-13-3323-1
950 $a Intelligent Technologies and Robotics (Springer-42732)