Description |
1 online resource (ix, 188 pages) : illustrations |
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text txt rdacontent |
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computer c rdamedia |
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online resource cr rdacarrier |
Series |
ITpro collection
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Bibliography |
Includes bibliographical references and index. |
Note |
Description based on online resource; title from digital title page (viewed on March 28, 2022). |
Contents |
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: An Algorithm and Hardware Codesign Prototype; 6. Ensemble Spiking Networks on Edge Device; 7. SenseCamera: A Learning Based Multifunctional Smart Camera Prototype |
Summary |
Deep Learning on Edge Computing Devices: Design Challenges of Algorithm and Architecture focuses on hardware architecture and embedded deep learning, including neural networks. -- Edited summary from book. |
Subject |
Deep learning (Machine learning)
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Edge computing.
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Operating systems (Computers)
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Apprentissage profond.
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Systèmes d'exploitation (Ordinateurs)
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operating systems.
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Deep learning (Machine learning)
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Edge computing
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Added Author |
Liu, Haijun, author.
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Shi, Cong, author.
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Liu, Ji, author.
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Other Form: |
Print version: Zhou, Xichuan Deep Learning on Edge Computing Devices San Diego : Elsevier,c2022 9780323857833 |
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Print version: ZHOU, XICHUAN. DEEP LEARNING ON EDGE COMPUTING DEVICES. [S.l.] : ELSEVIER, 2022 0323857833 (OCoLC)1268111913 |
ISBN |
0323909272 electronic book |
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9780323909273 (electronic bk.) |
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9780323857833 |
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0323857833 |
Standard No. |
AU@ 000071341250 |
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UKMGB 020528596 |
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AU@ 000071981941 |
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