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Hardware Accelerator Systems for Artificial Intelligence and Machine Learning

  • 1st Edition, Volume 122 - March 28, 2021
  • Latest edition
  • Editors: Shiho Kim, Ganesh Chandra Deka
  • Language: English

Hardware Accelerator Systems for Artificial Intelligence and Machine Learning, Volume 122 delves into artificial Intelligence and the growth it has seen with the advent of Deep N… Read more

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Description

Hardware Accelerator Systems for Artificial Intelligence and Machine Learning, Volume 122 delves into artificial Intelligence and the growth it has seen with the advent of Deep Neural Networks (DNNs) and Machine Learning. Updates in this release include chapters on Hardware accelerator systems for artificial intelligence and machine learning, Introduction to Hardware Accelerator Systems for Artificial Intelligence and Machine Learning, Deep Learning with GPUs, Edge Computing Optimization of Deep Learning Models for Specialized Tensor Processing Architectures, Architecture of NPU for DNN, Hardware Architecture for Convolutional Neural Network for Image Processing, FPGA based Neural Network Accelerators, and much more.

Key features

  • Updates on new information on the architecture of GPU, NPU and DNN
  • Discusses In-memory computing, Machine intelligence and Quantum computing
  • Includes sections on Hardware Accelerator Systems to improve processing efficiency and performance

Readership

Final year Undergraduate student for Project on Embedded system, Master’s and PhD Scholars

Table of contents

1. Hardware accelerator systems for artificial intelligence and machine learning
Shiho Kim

2. Introduction to Hardware Accelerator Systems for Artificial Intelligence and Machine Learning
Neha Gupta

3. Deep Learning with GPUs
Won Woo Ro

4. Edge Computing Optimization of Deep Learning Models for Specialized Tensor Processing Architectures-Yuri Gordienko
Yuri Gordienko

5. Architecture of NPU for DNN
Kyuho Lee

6. Hardware Architecture for Convolutional Neural Network for Image Processing
Vardhana M

7. FPGA based Neural Network Accelerators
Joo-Young Kim

8. Energy-Efficient Deep Learning Inference on Edge Devices
Massimo Poncino

9. Hardware accelerator systems for Embedded systems
William Jinho Song

10. Generic Quantum Hardware Accelerators for Conventional systems
Parth Bir

11. Music recommender system using Restricted Boltzmann Machine with Implicit Feedback
Malaya Dutta Borah

12. Embedded system for Automated Monitoring in Agriculture and Healthcare
Prashanta Kumar Das

Product details

  • Edition: 1
  • Latest edition
  • Volume: 122
  • Published: March 28, 2021
  • Language: English

About the editors

SK

Shiho Kim

Shiho Kim is a professor in the school of integrated technology at Yonsei University, Seoul, Korea. His previous assignment includes, System on chip design engineer, at LG Semicon Ltd. (currently SK Hynix), Korea, Seoul [1995-1996], Director of RAVERS (Research center for Advanced Hybrid Electric Vehicle Energy Recovery System, a government-supported IT research center. Associate Director of the ICT consilience program, which is a Korea National program for cultivating talented engineers in the field of information and communication Technology, Korea [2011-2012], Director of Seamless Transportation Lab, at Yonsei university, Korea [since 2011-]. His main research interest includes Development of software and hardware technologies for intelligent vehicles, Blockchain technology for intelligent transportation systems, and reinforcement learning for autonomous vehicles. He is the member of the editorial board and reviewer for various Journals and International conferences. So far he has organized 2 International Conference as Technical Chair/General Chair. He is a member of IEIE (Institute of Electronics and Information Engineers of Korea), KSAE (Korean Society of Automotive Engineers), vice president of KINGC (Korean Institute of Next Generation Computing), and a senior member of IEEE. He is the co-author for over 100 papers and holding more than 50 patents in the area of information and communication technology.
Affiliations and expertise
School of Integrated Technology, Yonsei University, Seoul, Korea

GD

Ganesh Chandra Deka

Ganesh Chandra Deka is currently Deputy Director (Training) at Directorate General of Training, Ministry of Skill Development and Entrepreneurship, Government of India, New Delhi-110001, India. His research interests include e-Governance, Big Data Analytics, NoSQL Databases and Vocational Education and Training. He has 2 books on Cloud Computing published by LAP Lambert, Germany. He is the Co-author for 4 text books on Fundamentals of Computer Science (3 books published by Moni Manik Prakashan, Guwahati, Assam, India and 1 IGI Global, USA). As of now he has edited 14 books (6 IGI Global, USA, 5 CRC Press, USA, 2 Elsevier & 1 Springer) on Big data, NoSQL and Cloud Computing and authored 10 Book Chapters. He has published around 47 research papers in various IEEE conferences. He has organized 08 IEEE International Conferences as Technical Chair in India. He is the Member of the editorial board and reviewer for various Journals and International conferences. Member of IEEE, the Institution of Electronics and Telecommunication Engineers, India and Associate Member, the Institution of Engineers, India
Affiliations and expertise
Ministry of Skill Development and Entrepreneurship, New Delhi, India

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