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Deep learning on edge computing devicesdesign challenges of algorithm and architecture /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Deep learning on edge computing devicesXichuan Zhou [and three others].
Reminder of title:	design challenges of algorithm and architecture /
Author:	Zhou, Xichuan.
other author:	Liu, Haijun.
Published:	Amsterdam ;Elsevier,2022.
Description:	1 online resource (ix, 188 p.) :ill.
Subject:	Deep learning (Machine learning)
Online resource:	https://www.sciencedirect.com/science/book/9780323857833
ISBN:	9780323909273$q(ebook)

Deep learning on edge computing devicesdesign challenges of algorithm and architecture /
Zhou, Xichuan.

Deep learning on edge computing devicesdesign challenges of algorithm and architecture /[electronic resource] :Xichuan Zhou [and three others]. - Amsterdam ;Elsevier,2022. - 1 online resource (ix, 188 p.) :ill. - ITpro collection.

Includes bibliographical references and index.

Part 1. Introduction ; 1. Introduction; ; Part 2. Theory and Algorithm ; 2. Model Inference on Edge Device; 3. Model Training on Edge Device; 4. Network Encoding and Quantization; ; Part 3. Architecture Optimization ; 5. DANoC: AnAlgorithm and Hardware Codesign Prototype; 6. Ensemble Spiking Networks on Edge Device; 7. SenseCamera: A Learning Based Multifunctional Smart Camera Prototype

Deep Learning on Edge Computing Devices: Design Challenges of Algorithm and Architecture focuses on hardware architecture and embedded deep learning, including neural networks. --

ISBN: 9780323909273$q(ebook)Subjects--Topical Terms:

913129
Deep learning (Machine learning)
Index Terms--Genre/Form:

214472
Electronic books.

LC Class. No.: Q325.73 / .Z46 2022eb

Dewey Class. No.: 006.3/1

Deep learning on edge computing devicesdesign challenges of algorithm and architecture /
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300 $a 1 online resource (ix, 188 p.) : $b ill.
490 0 $a ITpro collection
504 $a Includes bibliographical references and index.
505 0 $a Part 1. Introduction ; 1. Introduction; ; Part 2. Theory and Algorithm ; 2. Model Inference on Edge Device; 3. Model Training on Edge Device; 4. Network Encoding and Quantization; ; Part 3. Architecture Optimization ; 5. DANoC: AnAlgorithm and Hardware Codesign Prototype; 6. Ensemble Spiking Networks on Edge Device; 7. SenseCamera: A Learning Based Multifunctional Smart Camera Prototype
520 $a Deep Learning on Edge Computing Devices: Design Challenges of Algorithm and Architecture focuses on hardware architecture and embedded deep learning, including neural networks. -- $c Edited summary from book.
650 0 $a Deep learning (Machine learning) $3 913129
650 0 $a Edge computing. $3 875964
650 0 $a Operating systems (Computers) $3 182106
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700 1 $a Liu, Haijun. $3 994375
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776 0 8 $i Print version: $a Zhou, Xichuan $t Deep Learning on Edge Computing Devices $d San Diego : Elsevier,c2022 $z 9780323857833
776 0 8 $i Print version: $a ZHOU, XICHUAN. $t DEEP LEARNING ON EDGE COMPUTING DEVICES. $d [S.l.] : ELSEVIER, 2022 $z 0323857833 $w (OCoLC)1268111913
856 4 0 $u https://www.sciencedirect.com/science/book/9780323857833