Hardware Architectures for Deep Learning
Language: English
Published by Institution of Engineering and Technology, GB, 2020
- Hardcover
- New
Seller: Rarewaves.com UK, London, United KingdomRarewaves.com UK
AbeBooks seller since June 11, 2025
Condition: New
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Add to basketItem description from seller
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. …
Seller Inventory # LU-9781785617683
- Title
- Hardware Architectures for Deep Learning
- Author
- Masoud Daneshtalab
- Publisher
- Institution of Engineering and Technology, GB
- Publication year
- 2020
- Condition
- New
- Binding
- Hardback
- Language
- English
- ISBN 10
- 1785617680
- ISBN 13
- 9781785617683
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.
"Synopsis" may belong to another edition of this title.
About the Author
Masoud Daneshtalab is a tenured associate professor at Mälardalen University (MDH) in Sweden, an adjunct professor at Tallinn University of Technology (TalTech) in Estonia, and sits on the board of directors of Euromicro. His research interests include interconnection networks, brain-like computing, and deep learning architectures. He has published over 300-refereed papers.
Mehdi Modarressi is an assistant professor at the Department of Electrical and Computer Engineering, University of Tehran, Iran. He is the founder and director of the Parallel and Network-based Processing research laboratory at the University of Tehran, where he leads several industrial and research projects on deep learning-based embedded system design and implementation.
"About the title" may belong to another edition of this title.
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