Hardware Accelerator Systems for Artificial Intelligence and Machine Learning, Volume 122 delves into arti?cial 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,…mehr
Hardware Accelerator Systems for Artificial Intelligence and Machine Learning, Volume 122 delves into arti?cial 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.
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.
Inhaltsangabe
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
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
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