Hardware Accelerator Systems for Artificial Intelligence and Machine Learning

by Shiho Kim (Editor), Ganesh Chandra Deka (Editor)

Overview

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. - 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

Product Details

ISBN-13:	9780128231241
Publisher:	Elsevier Science & Technology Books
Publication date:	03/28/2021
Series:	Advances in Computers , #122
Sold by:	Barnes & Noble
Format:	eBook
Pages:	416
File size:	43 MB
Note:	This product may take a few minutes to download.

About the Author

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.
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

Table of Contents

1. Hardware accelerator systems for artificial intelligence and machine learningShiho Kim2. Introduction to Hardware Accelerator Systems for Artificial Intelligence and Machine LearningNeha Gupta3. Deep Learning with GPUsWon Woo Ro4. Edge Computing Optimization of Deep Learning Models for Specialized Tensor Processing Architectures-Yuri GordienkoYuri Gordienko5. Architecture of NPU for DNNKyuho Lee6. Hardware Architecture for Convolutional Neural Network for Image ProcessingVardhana M7. FPGA based Neural Network AcceleratorsJoo-Young Kim8. Energy-Efficient Deep Learning Inference on Edge DevicesMassimo Poncino9. Hardware accelerator systems for Embedded systemsWilliam Jinho Song10. Generic Quantum Hardware Accelerators for Conventional systemsParth Bir11. Music recommender system using Restricted Boltzmann Machine with Implicit FeedbackMalaya Dutta Borah12. Embedded system for Automated Monitoring in Agriculture and HealthcarePrashanta Kumar Das

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From the Publisher

Explores research trends in the architecture of GPU, DNN and NPU