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A methodology for using domino logic in an ASIC design flow for graduate students, researchers, and circuit designers in industry.
This book carefully details design tools and techniques for high-performance ASIC design. Using these techniques, the performance of ASIC designs can be improved by two to three times. Important topics include: Improving performance through microarchitecture; Timing-driven floorplanning; Controlling and exploiting clock skew; High performance latch-based design in an ASIC methodology; Automatically identifying and synthesizing complex logic gates; Automated cell sizing to increase performance and reduce power; Controlling process variation.These techniques are illustrated by designs running two to three times the speed of typical ASICs in the same process generation.
The latest techniques for designing robust, high performance integrated circuits in nanoscale technologies Focusing on a new technological paradigm, this practical guide describes the interconnect-centric design methodologies that are now the major focus of nanoscale integrated circuits (ICs). High Performance Integrated Circuit Design begins by discussing the dominant role of on-chip interconnects and provides an overview of technology scaling. The book goes on to cover data signaling, power management, synchronization, and substrate-aware design. Specific design constraints and methodologies unique to each type of interconnect are addressed. This comprehensive volume also explains the design of specialized circuits such as tapered buffers and repeaters for data signaling, voltage regulators for power management, and phase-locked loops for synchronization. This is an invaluable resource for students, researchers, and engineers working in the area of high performance ICs. Coverage includes: Technology scaling Interconnect modeling and extraction Signal propagation and delay analysis Interconnect coupling noise Global signaling Power generation Power distribution networks CAD of power networks Techniques to reduce power supply noise Power dissipation Synchronization theory and tradeoffs Synchronous system characteristics On-chip clock generation and distribution Substrate noise in mixed-signal ICs Techniques to reduce substrate noise
Over the past several decades, applications permeated by advances in digital signal processing have undergone unprecedented growth in capabilities. The editors and authors of High Performance Embedded Computing Handbook: A Systems Perspective have been significant contributors to this field, and the principles and techniques presented in the handbook are reinforced by examples drawn from their work. The chapters cover system components found in today’s HPEC systems by addressing design trade-offs, implementation options, and techniques of the trade, then solidifying the concepts with specific HPEC system examples. This approach provides a more valuable learning tool, Because readers learn about these subject areas through factual implementation cases drawn from the contributing authors’ own experiences. Discussions include: Key subsystems and components Computational characteristics of high performance embedded algorithms and applications Front-end real-time processor technologies such as analog-to-digital conversion, application-specific integrated circuits, field programmable gate arrays, and intellectual property–based design Programmable HPEC systems technology, including interconnection fabrics, parallel and distributed processing, performance metrics and software architecture, and automatic code parallelization and optimization Examples of complex HPEC systems representative of actual prototype developments Application examples, including radar, communications, electro-optical, and sonar applications The handbook is organized around a canonical framework that helps readers navigate through the chapters, and it concludes with a discussion of future trends in HPEC systems. The material is covered at a level suitable for practicing engineers and HPEC computational practitioners and is easily adaptable to their own implementation requirements.
Explains how to use low power design in an automated design flow, and examine the design time and performance trade-offs Includes the latest tools and techniques for low power design applied in an ASIC design flow Focuses on low power in an automated design methodology, a much neglected area
This book describes simple to complex ASIC design practical scenarios using Verilog. It builds a story from the basic fundamentals of ASIC designs to advanced RTL design concepts using Verilog. Looking at current trends of miniaturization, the contents provide practical information on the issues in ASIC design and synthesis using Synopsys DC and their solution. The book explains how to write efficient RTL using Verilog and how to improve design performance. It also covers architecture design strategies, multiple clock domain designs, low-power design techniques, DFT, pre-layout STA and the overall ASIC design flow with case studies. The contents of this book will be useful to practicing hardware engineers, students, and hobbyists looking to learn about ASIC design and synthesis.
This book constitutes the refereed proceedings of the 12th International Conference on High-Performance Computing, HiPC 2005, held in Goa, India in December 2005. The 50 revised full papers presented were carefully reviewed and selected from 362 submissions. After the keynote section and the presentation of the 2 awarded best contributions the papers are organized in topical sections on algorithms, applications, architecture, systems software, communication networks, and systems and networks.
The processing of medical images in a reasonable timeframe and with high definition is very challenging. This volume helps to meet that challenge by presenting a thorough overview of medical imaging modalities, its processing, high-performance computing, and the need to embed parallelism in medical image processing techniques to achieve efficient and fast results. With contributions from researchers from prestigious laboratories and educational institutions, High-Performance Medical Image Processing provides important information on medical image processing techniques, parallel computing techniques, and embedding parallelism in different image processing techniques. A comprehensive review of parallel algorithms in medical image processing problems is a key feature of this book. The volume presents the relevant theoretical frameworks and the latest empirical research findings in the area and provides detailed descriptions about the diverse high-performance techniques. Topics discussed include parallel computing, multicore architectures and their applications in image processing, machine learning applications, conventional and advanced magnetic resonance imaging methods, hyperspectral image processing, algorithms for segmenting 2D slices for 3D viewing, and more. Case studies, such as on the detection of cancer tumors, expound on the information presented. Key features: Provides descriptions of different medical imaging modalities and their applications Discusses the basics and advanced aspects of parallel computing with different multicore architectures Expounds on the need for embedding data and task parallelism in different medical image processing techniques Presents helpful examples and case studies of the discussed methods This book will be valuable for professionals, researchers, and students working in the field of healthcare engineering, medical imaging technology, applications in machine and deep learning, and more. It is also appropriate for courses in computer engineering, biomedical engineering and electrical engineering based on artificial intelligence, parallel computing, high performance computing, and machine learning and its applications in medical imaging.
From the Foreword..... Modern digital signal processing applications provide a large challenge to the system designer. Algorithms are becoming increasingly complex, and yet they must be realized with tight performance constraints. Nevertheless, these DSP algorithms are often built from many constituent canonical subtasks (e.g., IIR and FIR filters, FFTs) that can be reused in other subtasks. Design is then a problem of composing these core entities into a cohesive whole to provide both the intended functionality and the required performance. In order to organize the design process, there have been two major approaches. The top-down approach starts with an abstract, concise, functional description which can be quickly generated. On the other hand, the bottom-up approach starts from a detailed low-level design where performance can be directly assessed, but where the requisite design and interface detail take a long time to generate. In this book, the authors show a way to effectively resolve this tension by retaining the high-level conciseness of VHDL while parameterizing it to get good fit to specific applications through reuse of core library components. Since they build on a pre-designed set of core elements, accurate area, speed and power estimates can be percolated to high- level design routines which explore the design space. Results are impressive, and the cost model provided will prove to be very useful. Overall, the authors have provided an up-to-date approach, doing a good job at getting performance out of high-level design. The methodology provided makes good use of extant design tools, and is realistic in terms of the industrial design process. The approach is interesting in its own right, but is also of direct utility, and it will give the existing DSP CAD tools a highly competitive alternative. The techniques described have been developed within ARPAs RASSP (Rapid Prototyping of Application Specific Signal Processors) project, and should be of great interest there, as well as to many industrial designers. Professor Jonathan Allen, Massachusetts Institute of Technology
High-Performance Computing for Big Data: Methodologies and Applications explores emerging high-performance architectures for data-intensive applications, novel efficient analytical strategies to boost data processing, and cutting-edge applications in diverse fields, such as machine learning, life science, neural networks, and neuromorphic engineering. The book is organized into two main sections. The first section covers Big Data architectures, including cloud computing systems, and heterogeneous accelerators. It also covers emerging 3D IC design principles for memory architectures and devices. The second section of the book illustrates emerging and practical applications of Big Data across several domains, including bioinformatics, deep learning, and neuromorphic engineering. Features Covers a wide range of Big Data architectures, including distributed systems like Hadoop/Spark Includes accelerator-based approaches for big data applications such as GPU-based acceleration techniques, and hardware acceleration such as FPGA/CGRA/ASICs Presents emerging memory architectures and devices such as NVM, STT- RAM, 3D IC design principles Describes advanced algorithms for different big data application domains Illustrates novel analytics techniques for Big Data applications, scheduling, mapping, and partitioning methodologies Featuring contributions from leading experts, this book presents state-of-the-art research on the methodologies and applications of high-performance computing for big data applications. About the Editor Dr. Chao Wang is an Associate Professor in the School of Computer Science at the University of Science and Technology of China. He is the Associate Editor of ACM Transactions on Design Automations for Electronics Systems (TODAES), Applied Soft Computing, Microprocessors and Microsystems, IET Computers & Digital Techniques, and International Journal of Electronics. Dr. Chao Wang was the recipient of Youth Innovation Promotion Association, CAS, ACM China Rising Star Honorable Mention (2016), and best IP nomination of DATE 2015. He is now on the CCF Technical Committee on Computer Architecture, CCF Task Force on Formal Methods. He is a Senior Member of IEEE, Senior Member of CCF, and a Senior Member of ACM.