Hardware Accelerator Systems for Artificial Intelligence and Machine Learning
Academic Press Inc (Verlag)
978-0-12-823123-4 (ISBN)
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
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
Erscheinungsdatum | 06.04.2021 |
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Reihe/Serie | Advances in Computers |
Verlagsort | San Diego |
Sprache | englisch |
Maße | 152 x 229 mm |
Gewicht | 810 g |
Themenwelt | Mathematik / Informatik ► Informatik ► Software Entwicklung |
Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik | |
Mathematik / Informatik ► Mathematik | |
ISBN-10 | 0-12-823123-8 / 0128231238 |
ISBN-13 | 978-0-12-823123-4 / 9780128231234 |
Zustand | Neuware |
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