Hoi-Jun Yoo – författare

The first book to explain the principals behind mobile 3D hardware implementation, helping readers understand advanced algorithms, produce low-cost, low-power SoCs, or become familiar with embedded systems As mobile broadcasting and entertainment applications evolve, there is increasing interest in 3D graphics within the field of mobile electronics, particularly for handheld devices. In Mobile 3D Graphics SoC, Yoo provides a comprehensive understanding of the algorithms of mobile 3D graphics and their real chip implementation methods. 3D graphics SoC (System on a Chip) architecture and its interaction with embedded system software are explained with numerous examples. Yoo divides the book into three sections: general methodology of low power SoC, design of low power 3D graphics SoC, and silicon implementation of 3D graphics SoCs and their application to mobile electronics. Full examples are presented at various levels such as system level design and circuit level optimization along with design technology. Yoo incorporates many real chip examples, including many commercial 3D graphics chips, and provides cross-comparisons of various architectures and their performance. Furthermore, while advanced 3D graphics techniques are well understood and supported by industry standards, this is less true in the emerging mobile applications and games market. This book redresses this imbalance, providing an in-depth look at the new OpenGL ES (The Standard for Embedded Accelerated 3D Graphics), and shows what these new embedded systems graphics libraries can provide for 3D graphics and games developers.

Chip Design and Implementation from a Practical ViewpointFocusing on chip implementation, Low-Power NoC for High-Performance SoC Design provides practical knowledge and real examples of how to use network on chip (NoC) in the design of system on chip (SoC). It discusses many architectural and theoretical studies on NoCs, including design methodology, topology exploration, quality-of-service guarantee, low-power design, and implementation trials. The Steps to Implement NoC The book covers the full spectrum of the subject, from theory to actual chip design using NoC. Employing the Unified Modeling Language (UML) throughout, it presents complicated concepts, such as models of computation and communication–computation partitioning, in a manner accessible to laypeople. The authors provide guidelines on how to simplify complex networking theory to design a working chip. In addition, they explore the novel NoC techniques and implementations of the Basic On-Chip Network (BONE) project. Examples of real-time decisions, circuit-level design, systems, and chips give the material a real-world context. Low-Power NoC and Its Application to SoC Design Emphasizing the application of NoC to SoC design, this book shows how to build the complicated interconnections on SoC while keeping a low power consumption.

This book is based on a graduate course entitled, Ubiquitous Healthcare Circuits and Systems, that was given by one of the editors at his university.  It includes an introduction and overview to the field of biomedical ICs and provides information on the current trends in research.  The material focuses on the design of biomedical ICs rather than focusing on how to use prepared ICs.

This book is based on a graduate course entitled, Ubiquitous Healthcare Circuits and Systems, that was given by one of the editors at his university.  It includes an introduction and overview to the field of biomedical ICs and provides information on the current trends in research.  The material focuses on the design of biomedical ICs rather than focusing on how to use prepared ICs.

Unlike most available sources that focus on deep neural network (DNN) inference, this book provides readers with a single-source reference on the needs, requirements, and challenges involved with on-device, DNN training semiconductor and SoC design. The authors include coverage of the trends and history surrounding the development of on-device DNN training, as well as on-device training semiconductors and SoC design examples to facilitate understanding.

Unlike most available sources that focus on deep neural network (DNN) inference, this book provides readers with a single-source reference on the needs, requirements, and challenges involved with on-device, DNN training semiconductor and SoC design.

This book discusses the acceleration of deep reinforcement learning (DRL), which may be the next step in the burst success of artificial intelligence (AI). The authors address acceleration systems which enable DRL on area-limited & battery-limited mobile devices. Methods are described that enable DRL optimization at the algorithm-, architecture-, and circuit-levels of abstraction.