Robust Nano-Computing focuses on various issues of robust nano-computing, defect-tolerance design for nano-technology at different design abstraction levels. It addresses both redundancy- and configuration-based methods as well as fault detecting techniques through the development of accurate computation models and tools. The contents present an insightful view of the ongoing researches on nano-electronic devices, circuits, architectures, and design methods, as well as provide promising directions for future research.
Robust Nano-Computing focuses on various issues of robust nano-computing, defect-tolerance design for nano-technology at different design abstraction levels. It addresses both redundancy- and configuration-based methods as well as fault detecting techniques through the development of accurate computation models and tools. The contents present an insightful view of the ongoing researches on nano-electronic devices, circuits, architectures, and design methods, as well as provide promising directions for future research.
One of the grand challenges in the nano-scopic computing era is guarantees of robustness. Robust computing system design is confronted with quantum physical, probabilistic, and even biological phenomena, and guaranteeing high reliability is much more difficult than ever before. Scaling devices down to the level of single electron operation will bring forth new challenges due to probabilistic effects and uncertainty in guaranteeing 'zero-one' based computing. Minuscule devices imply billions of devices on a single chip, which may help mitigate the challenge of uncertainty by replication and redundancy. However, such device densities will create a design and validation nightmare with the shear scale. The questions that confront computer engineers regarding the current status of nanocomputing material and the reliability of systems built from such miniscule devices, are difficult to articulate and answer. We have found a lack of resources in the confines of a single volume that at least partially attempts to answer these questions. We believe that this volume contains a large amount of research material as well as new ideas that will be very useful for some one starting research in the arena of nanocomputing, not at the device level, but the problems one would face at system level design and validation when nanoscopic physicality will be present at the device level.
This book provides comprehensive coverage of the dependability challenges in today's advanced computing systems. It is an in-depth discussion of all the technological and design-level techniques that may be used to overcome these issues and analyzes various dependability-assessment methods. The impact of individual application scenarios on the definition of challenges and solutions is considered so that the designer can clearly assess the problems and adjust the solution based on the specifications in question. The book is composed of three sections, beginning with an introduction to current dependability challenges arising in complex computing systems implemented with nanoscale technologies, and of the effect of the application scenario. The second section details all the fault-tolerance techniques that are applicable in the manufacture of reliable advanced computing devices. Different levels, from technology-level fault avoidance to the use of error correcting codes and system-level checkpointing are introduced and explained as applicable to the different application scenario requirements. Finally the third section proposes a roadmap of future trends in and perspectives on the dependability and manufacturability of advanced computing systems from the special point of view of industrial stakeholders. Dependable Multicore Architectures at Nanoscale showcases the original ideas and concepts introduced into the field of nanoscale manufacturing and systems reliability over nearly four years of work within COST Action IC1103 MEDIAN, a think-tank with participants from 27 countries. Academic researchers and graduate students working in multi-core computer systems and their manufacture will find this book of interest as will industrial design and manufacturing engineers working in VLSI companies.
Explore the potential for nanotechnologies to transform future mobile and Internet communications. Based on a research collaboration between Nokia, Helsinki University of Technology, and the University of Cambridge, here leading researchers review the current state-of-the art and future prospects for: • Novel multifunctional materials, dirt repellent, self-healing surface materials, and lightweight structural materials capable of adapting their shape • Portable energy storage using supercapacitor-battery hybrids based on new materials including carbon nanohorns and porous electrodes, fuel cell technologies, energy harvesting and more efficient solar cells • Electronics and computing advances reaching beyond IC scaling limits, new computing approaches and architectures, embedded intelligence and future memory technologies. • Nanoscale transducers for mechanical, optical and chemical sensing, sensor signal processing, and nanoscale actuation • Nanoelectronics to create ultrafast and adaptive electronics for future radio technologies • Flat panel displays with greater robustness, improved resolution, brightness and contrast, and mechanical flexibility • Manufacturing and innovation processes, plus commercialization of nanotechnologies.
The primary aim of this book is to discuss various aspects of nanoscale device design and their applications including transport mechanism, modeling, and circuit applications. . Provides a platform for modeling and analysis of state-of-the-art devices in nanoscale regime, reviews issues related to optimizing the sub-nanometer device performance and addresses simulation aspect and/or fabrication process of devices Also, includes design problems at the end of each chapter
This reference text covers a wide spectrum for designing robust embedded memory and peripheral circuitry. It will serve as a useful text for senior undergraduate and graduate students and professionals in areas including electronics and communications engineering, electrical engineering, mechanical engineering, and aerospace engineering. Discusses low-power design methodologies for static random-access memory (SRAM) Covers radiation-hardened SRAM design for aerospace applications Focuses on various reliability issues that are faced by submicron technologies Exhibits more stable memory topologies Nanoscale technologies unveiled significant challenges to the design of energy- efficient and reliable SRAMs. This reference text investigates the impact of process variation, leakage, aging, soft errors and related reliability issues in embedded memory and periphery circuitry. The text adopts a unique way to explain the SRAM bitcell, array design, and analysis of its design parameters to meet the sub-nano-regime challenges for complementary metal-oxide semiconductor devices. It comprehensively covers low- power-design methodologies for SRAM, exhibits more stable memory topologies, and radiation-hardened SRAM design for aerospace applications. Every chapter includes a glossary, highlights, a question bank, and problems. The text will serve as a useful text for senior undergraduate students, graduate students, and professionals in areas including electronics and communications engineering, electrical engineering, mechanical engineering, and aerospace engineering. Discussing comprehensive studies of variability-induced failure mechanism in sense amplifiers and power, delay, and read yield trade-offs, this reference text will serve as a useful text for senior undergraduate, graduate students, and professionals in areas including electronics and communications engineering, electrical engineering, mechanical engineering, and aerospace engineering. It covers the development of robust SRAMs, well suited for low-power multi-core processors for wireless sensors node, battery-operated portable devices, personal health care assistants, and smart Internet of Things applications.
This book comprehensively and systematically treats modern understanding of the Nano-Bio-Technology and its therapeutic applications. The contents range from the nanomedicine, imaging, targeted therapeutic applications, experimental results along with modelling approaches. It will provide the readers with fundamentals on computational and modelling aspects of advanced nano-materials and nano-technology specifically in the field of biomedicine, and also provide the readers with inspirations for new development of diagnostic imaging and targeted therapeutic applications.
Explains current co-design and co-optimization methodologies for building hardware neural networks and algorithms for machine learning applications This book focuses on how to build energy-efficient hardware for neural networks with learning capabilities—and provides co-design and co-optimization methodologies for building hardware neural networks that can learn. Presenting a complete picture from high-level algorithm to low-level implementation details, Learning in Energy-Efficient Neuromorphic Computing: Algorithm and Architecture Co-Design also covers many fundamentals and essentials in neural networks (e.g., deep learning), as well as hardware implementation of neural networks. The book begins with an overview of neural networks. It then discusses algorithms for utilizing and training rate-based artificial neural networks. Next comes an introduction to various options for executing neural networks, ranging from general-purpose processors to specialized hardware, from digital accelerator to analog accelerator. A design example on building energy-efficient accelerator for adaptive dynamic programming with neural networks is also presented. An examination of fundamental concepts and popular learning algorithms for spiking neural networks follows that, along with a look at the hardware for spiking neural networks. Then comes a chapter offering readers three design examples (two of which are based on conventional CMOS, and one on emerging nanotechnology) to implement the learning algorithm found in the previous chapter. The book concludes with an outlook on the future of neural network hardware. Includes cross-layer survey of hardware accelerators for neuromorphic algorithms Covers the co-design of architecture and algorithms with emerging devices for much-improved computing efficiency Focuses on the co-design of algorithms and hardware, which is especially critical for using emerging devices, such as traditional memristors or diffusive memristors, for neuromorphic computing Learning in Energy-Efficient Neuromorphic Computing: Algorithm and Architecture Co-Design is an ideal resource for researchers, scientists, software engineers, and hardware engineers dealing with the ever-increasing requirement on power consumption and response time. It is also excellent for teaching and training undergraduate and graduate students about the latest generation neural networks with powerful learning capabilities.
