High-level Synthesis


Book Description

Are you an RTL or system designer that is currently using, moving, or planning to move to an HLS design environment? Finally, a comprehensive guide for designing hardware using C++ is here. Michael Fingeroff's High-Level Synthesis Blue Book presents the most effective C++ synthesis coding style for achieving high quality RTL. Master a totally new design methodology for coding increasingly complex designs! This book provides a step-by-step approach to using C++ as a hardware design language, including an introduction to the basics of HLS using concepts familiar to RTL designers. Each chapter provides easy-to-understand C++ examples, along with hardware and timing diagrams where appropriate. The book progresses from simple concepts such as sequential logic design to more complicated topics such as memory architecture and hierarchical sub-system design. Later chapters bring together many of the earlier HLS design concepts through their application in simplified design examples. These examples illustrate the fundamental principles behind C++ hardware design, which will translate to much larger designs. Although this book focuses primarily on C and C++ to present the basics of C++ synthesis, all of the concepts are equally applicable to SystemC when describing the core algorithmic part of a design. On completion of this book, readers should be well on their way to becoming experts in high-level synthesis.




High-Level Synthesis


Book Description

This book presents an excellent collection of contributions addressing different aspects of high-level synthesis from both industry and academia. It includes an overview of available EDA tool solutions and their applicability to design problems.







Recent Findings in Intelligent Computing Techniques


Book Description

This three volume book contains the Proceedings of 5th International Conference on Advanced Computing, Networking and Informatics (ICACNI 2017). The book focuses on the recent advancement of the broad areas of advanced computing, networking and informatics. It also includes novel approaches devised by researchers from across the globe. This book brings together academic scientists, professors, research scholars and students to share and disseminate information on knowledge and scientific research works related to computing, networking, and informatics to discuss the practical challenges encountered and the solutions adopted. The book also promotes translation of basic research into applied investigation and convert applied investigation into practice.




Design of Cost-Efficient Interconnect Processing Units


Book Description

Streamlined Design Solutions Specifically for NoC To solve critical network-on-chip (NoC) architecture and design problems related to structure, performance and modularity, engineers generally rely on guidance from the abundance of literature about better-understood system-level interconnection networks. However, on-chip networks present several distinct challenges that require novel and specialized solutions not found in the tried-and-true system-level techniques. A Balanced Analysis of NoC Architecture As the first detailed description of the commercial Spidergon STNoC architecture, Design of Cost-Efficient Interconnect Processing Units: Spidergon STNoC examines the highly regarded, cost-cutting technology that is set to replace well-known shared bus architectures, such as STBus, for demanding multiprocessor system-on-chip (SoC) applications. Employing a balanced, well-organized structure, simple teaching methods, numerous illustrations, and easy-to-understand examples, the authors explain: how the SoC and NoC technology works why developers designed it the way they did the system-level design methodology and tools used to configure the Spidergon STNoC architecture differences in cost structure between NoCs and system-level networks From professionals in computer sciences, electrical engineering, and other related fields, to semiconductor vendors and investors – all readers will appreciate the encyclopedic treatment of background NoC information ranging from CMPs to the basics of interconnection networks. The text introduces innovative system-level design methodology and tools for efficient design space exploration and topology selection. It also provides a wealth of key theoretical and practical MPSoC and NoC topics, such as technological deep sub-micron effects, homogeneous and heterogeneous processor architectures, multicore SoC, interconnect processing units, generic NoC components, and embeddings of common communication patterns.




Low-Power High-Level Synthesis for Nanoscale CMOS Circuits


Book Description

This self-contained book addresses the need for analysis, characterization, estimation, and optimization of the various forms of power dissipation in the presence of process variations of nano-CMOS technologies. The authors show very large-scale integration (VLSI) researchers and engineers how to minimize the different types of power consumption of digital circuits. The material deals primarily with high-level (architectural or behavioral) energy dissipation.




Machine Learning in VLSI Computer-Aided Design


Book Description

This book provides readers with an up-to-date account of the use of machine learning frameworks, methodologies, algorithms and techniques in the context of computer-aided design (CAD) for very-large-scale integrated circuits (VLSI). Coverage includes the various machine learning methods used in lithography, physical design, yield prediction, post-silicon performance analysis, reliability and failure analysis, power and thermal analysis, analog design, logic synthesis, verification, and neuromorphic design. Provides up-to-date information on machine learning in VLSI CAD for device modeling, layout verifications, yield prediction, post-silicon validation, and reliability; Discusses the use of machine learning techniques in the context of analog and digital synthesis; Demonstrates how to formulate VLSI CAD objectives as machine learning problems and provides a comprehensive treatment of their efficient solutions; Discusses the tradeoff between the cost of collecting data and prediction accuracy and provides a methodology for using prior data to reduce cost of data collection in the design, testing and validation of both analog and digital VLSI designs. From the Foreword As the semiconductor industry embraces the rising swell of cognitive systems and edge intelligence, this book could serve as a harbinger and example of the osmosis that will exist between our cognitive structures and methods, on the one hand, and the hardware architectures and technologies that will support them, on the other....As we transition from the computing era to the cognitive one, it behooves us to remember the success story of VLSI CAD and to earnestly seek the help of the invisible hand so that our future cognitive systems are used to design more powerful cognitive systems. This book is very much aligned with this on-going transition from computing to cognition, and it is with deep pleasure that I recommend it to all those who are actively engaged in this exciting transformation. Dr. Ruchir Puri, IBM Fellow, IBM Watson CTO & Chief Architect, IBM T. J. Watson Research Center




Low Power Design with High-Level Power Estimation and Power-Aware Synthesis


Book Description

This book presents novel research techniques, algorithms, methodologies and experimental results for high level power estimation and power aware high-level synthesis. Readers will learn to apply such techniques to enable design flows resulting in shorter time to market and successful low power ASIC/FPGA design.




Machine Learning Applications in Electronic Design Automation


Book Description

​This book serves as a single-source reference to key machine learning (ML) applications and methods in digital and analog design and verification. Experts from academia and industry cover a wide range of the latest research on ML applications in electronic design automation (EDA), including analysis and optimization of digital design, analysis and optimization of analog design, as well as functional verification, FPGA and system level designs, design for manufacturing (DFM), and design space exploration. The authors also cover key ML methods such as classical ML, deep learning models such as convolutional neural networks (CNNs), graph neural networks (GNNs), generative adversarial networks (GANs) and optimization methods such as reinforcement learning (RL) and Bayesian optimization (BO). All of these topics are valuable to chip designers and EDA developers and researchers working in digital and analog designs and verification.




VLSI and Hardware Implementations using Modern Machine Learning Methods


Book Description

Machine learning is a potential solution to resolve bottleneck issues in VLSI via optimizing tasks in the design process. This book aims to provide the latest machine-learning–based methods, algorithms, architectures, and frameworks designed for VLSI design. The focus is on digital, analog, and mixed-signal design techniques, device modeling, physical design, hardware implementation, testability, reconfigurable design, synthesis and verification, and related areas. Chapters include case studies as well as novel research ideas in the given field. Overall, the book provides practical implementations of VLSI design, IC design, and hardware realization using machine learning techniques. Features: Provides the details of state-of-the-art machine learning methods used in VLSI design Discusses hardware implementation and device modeling pertaining to machine learning algorithms Explores machine learning for various VLSI architectures and reconfigurable computing Illustrates the latest techniques for device size and feature optimization Highlights the latest case studies and reviews of the methods used for hardware implementation This book is aimed at researchers, professionals, and graduate students in VLSI, machine learning, electrical and electronic engineering, computer engineering, and hardware systems.