386,36 €
429,29 €
-10% with code: EXTRA
Deep Learning on Edge Computing Devices
386,36 € 
- We will send in 10–14 business days.
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 surveill…
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- Author: Xichuan Zhou
- Publisher: Elsevier
- ISBN-10: 0323857833
- ISBN-13: 9780323857833
- Format: 15.2 x 22.9 x 1.1 cm, softcover
- Language: English
- SAVE -10% with code: EXTRA
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Description
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.EXTRA 10 % discount with code: EXTRA
386,36 €
429,29 €
We will send in 10–14 business days.
The promotion ends in 14d.02:38:21
The discount code is valid when purchasing from 10 €. Discounts do not stack.
- Author: Xichuan Zhou
- Publisher: Elsevier
- ISBN-10: 0323857833
- ISBN-13: 9780323857833
- Format: 15.2 x 22.9 x 1.1 cm, softcover
- Language: English English
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.
Reviews