Deep learning on edge computing devices [electronic resource] : design challenges of algorithm and architecture / Xichuan Zhou [and three others].
- 作者： Zhou, Xichuan.
- 其他作者：
- 出版： Amsterdam ;Cambridge, MA : Elsevier 2022.
- 叢書名： ITpro collection
- 主題： Deep learning (Machine learning) , Edge computing. , Operating systems (Computers) , Electronic books.
- ISBN： 9780323909273 (ebook) 、 0323909272 (ebook)
- URL：
電子書(校內)
電子書(校外)
- 一般註：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 114年度臺灣學術電子書暨資料庫聯盟採購
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讀者標籤：
- 系統號： 000327428 | 機讀編目格式
館藏資訊
Deep Learning on Edge Computing Devices: Design Challenges of Algorithm and Architecture focuses on hardware architecture and embedded deep learning, including neural networks. The title helps researchers maximize the performance of Edge-deep learning models for mobile computing and other applications by presenting neural network algorithms and hardware design optimization approaches for Edge-deep learning. Applications are introduced in each section, and a comprehensive example, smart surveillance cameras, is presented at the end of the book, integrating innovation in both algorithm and hardware architecture. Structured into three parts, the book covers core concepts, theories and algorithms and architecture optimization.This book provides a solution for researchers looking to maximize the performance of deep learning models on Edge-computing devices through algorithm-hardware co-design. - Focuses on hardware architecture and embedded deep learning, including neural networks - Brings together neural network algorithm and hardware design optimization approaches to deep learning, alongside real-world applications - Considers how Edge computing solves privacy, latency and power consumption concerns related to the use of the Cloud - Describes how to maximize the performance of deep learning on Edge-computing devices - Presents the latest research on neural network compression coding, deep learning algorithms, chip co-design and intelligent monitoring
摘要註
Deep Learning on Edge Computing Devices: Design Challenges of Algorithm and Architecture focuses on hardware architecture and embedded deep learning, including neural networks. --