Hardware Accelerator Systems for Artificial Intelligence and Machine Learning

Hardware Accelerator Systems for Artificial Intelligence and Machine Learning

1st Edition - March 28, 2021
This is the Latest Edition
  • Editors: Shiho Kim, Ganesh Deka
  • eBook ISBN: 9780128231241
  • Hardcover ISBN: 9780128231234

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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

  • No. of pages: 416
  • Language: English
  • Copyright: © Academic Press 2021
  • Published: March 28, 2021
  • Imprint: Academic Press
  • eBook ISBN: 9780128231241
  • Hardcover ISBN: 9780128231234

About the Serial Volume Editors

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

Ganesh Deka

Ganesh 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