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Deep learning on edge computing devices = design challenges of algorithm and architecture /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Deep learning on edge computing devices/ Xichuan 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

Deep learning on edge computing devices = design 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: 9780323909273Subjects--Topical Terms:

1381171
Deep learning (Machine learning)
Index Terms--Genre/Form:

554714
Electronic books.

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

Dewey Class. No.: 006.3/1

Deep learning on edge computing devices = design challenges of algorithm and architecture /
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300 $a 1 online resource (ix, 188 p.) : $b ill.
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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.
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