- We will send in 10–14 business days.
/images/books/12215764/9781785617683.jpg)
- Publisher: Institution of Engineering & Technology
- ISBN-10: 1785617680
- ISBN-13: 9781785617683
- Format: 16 x 23.9 x 2 cm, hardcover
- Language: English
- SAVE -10% with code: EXTRA
Hardware Architectures for Deep Learning (e-book) (used book) | bookbook.eu
Reviews
Description
This book presents and discusses innovative ideas in the design, modelling, implementation, and optimization of hardware platforms for neural networks.
The rapid growth of server, desktop, and embedded applications based on deep learning has brought about a renaissance in interest in neural networks, with applications including image and speech processing, data analytics, robotics, healthcare monitoring, and IoT solutions. Efficient implementation of neural networks to support complex deep learning-based applications is a complex challenge for embedded and mobile computing platforms with limited computational/storage resources and a tight power budget. Even for cloud-scale systems it is critical to select the right hardware configuration based on the neural network complexity and system constraints in order to increase power- and performance-efficiency.
Hardware Architectures for Deep Learning provides an overview of this new field, from principles to applications, for researchers, postgraduate students and engineers who work on learning-based services and hardware platforms.
EXTRA 10 % discount with code: EXTRA
The promotion ends in 19d.16:05:23
The discount code is valid when purchasing from 10 €. Discounts do not stack.
- Publisher: Institution of Engineering & Technology
- ISBN-10: 1785617680
- ISBN-13: 9781785617683
- Format: 16 x 23.9 x 2 cm, hardcover
- Language: English English
This book presents and discusses innovative ideas in the design, modelling, implementation, and optimization of hardware platforms for neural networks.
The rapid growth of server, desktop, and embedded applications based on deep learning has brought about a renaissance in interest in neural networks, with applications including image and speech processing, data analytics, robotics, healthcare monitoring, and IoT solutions. Efficient implementation of neural networks to support complex deep learning-based applications is a complex challenge for embedded and mobile computing platforms with limited computational/storage resources and a tight power budget. Even for cloud-scale systems it is critical to select the right hardware configuration based on the neural network complexity and system constraints in order to increase power- and performance-efficiency.
Hardware Architectures for Deep Learning provides an overview of this new field, from principles to applications, for researchers, postgraduate students and engineers who work on learning-based services and hardware platforms.
Reviews