Deep Learning on Edge Computing Devices: Design Challenges of Algorithm and Architecture
Language: English
Published by Elsevier, 2022
- Softcover
- Used
Seller: Books From California, Simi Valley, CA, U.S.A.Books From California
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- Title
- Deep Learning on Edge Computing Devices: Design Challenges of Algorithm and Architecture
- Author
- Zhou, Xichuan,Liu, Haijun,Shi, Cong,Liu, Ji
- Publisher
- Elsevier
- Publication year
- 2022
- Condition
- Very Good
- Binding
- paperback
- Language
- English
- ISBN 10
- 0323857833
- ISBN 13
- 9780323857833
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
"Synopsis" may belong to another edition of this title.
About the Author
Research Assistant, School of Microelectronics and Communication Engineering, Chongqing University, Chongqing, China. He received his B.Eng, M.Eng and Ph.D degree from University of Electronic Science and Technology of China in 2011, 2014 and 2019, and has been a visiting scholar of Kyoto University from 2018 to 2019. His main research interests include manifold learning, metric learning, deep learning, subspace clustering and sparse representation in computer vision and machine learning, with focuses on human action detection and recognition, face detection and recognition, person detection and re-identification, remote sensing image processing, and medical image analysis.
Cong Shi is a Research Professor in the School of Microelectronics and Communication Engineering, at Chongqing University, China. He received his PhD from Tsinghua University and has held the position of Postdoctoral Fellow with the Schepens Eye Research Institute, at Harvard Medical School. His research focuses on AI-based visual processing system-on-chips, and algorithm hardware co-design techniques. He has published over 30 papers.
Ji Liu is the Head of the AI platform department and the director of the Seattle AI lab for Kwai Inc. He has previously been a faculty member in computer science at the University of Rochester, USA. He received his PhD from the University of Wisconsin-Madison. His research includes machine learning, optimization, computer vision, reinforcement learning, and other areas. He has published over 100 papers.
"About the title" may belong to another edition of this title.
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