This book provides a comprehensive synthesis of the theory and practice of photonic devices for networks-on-chip. It outlines the issues in designing photonic network-on-chip architectures for future many-core high performance chip multiprocessors. The discussion is built from the bottom up: starting with the design and implementation of key photonic devices and building blocks, reviewing networking and network-on-chip theory and existing research, and finishing with describing various architectures, their characteristics, and the impact they will have on a computing system. After acquainting the reader with all the issues in the design space, the discussion concludes with design automation techniques, supplemented by provided software.
Silicon photonics is beginning to play an important role in driving innovations in communication and computation for an increasing number of applications, from health care and biomedical sensors to autonomous driving, datacenter networking, and security. In recent years, there has been a significant amount of effort in industry and academia to innovate, design, develop, analyze, optimize, and fabricate systems employing silicon photonics, shaping the future of not only Datacom and telecom technology but also high-performance computing and emerging computing paradigms, such as optical computing and artificial intelligence. Different from existing books in this area, Silicon Photonics for High-Performance Computing and Beyond presents a comprehensive overview of the current state-of-the-art technology and research achievements in applying silicon photonics for communication and computation. It focuses on various design, development, and integration challenges, reviews the latest advances spanning materials, devices, circuits, systems, and applications. Technical topics discussed in the book include: • Requirements and the latest advances in high-performance computing systems • Device- and system-level challenges and latest improvements to deploy silicon photonics in computing systems • Novel design solutions and design automation techniques for silicon photonic integrated circuits • Novel materials, devices, and photonic integrated circuits on silicon • Emerging computing technologies and applications based on silicon photonics Silicon Photonics for High-Performance Computing and Beyond presents a compilation of 19 outstanding contributions from academic and industry pioneers in the field. The selected contributions present insightful discussions and innovative approaches to understand current and future bottlenecks in high-performance computing systems and traditional computing platforms, and the promise of silicon photonics to address those challenges. It is ideal for researchers and engineers working in the photonics, electrical, and computer engineering industries as well as academic researchers and graduate students (M.S. and Ph.D.) in computer science and engineering, electronic and electrical engineering, applied physics, photonics, and optics.
This book provides comprehensive coverage of Network-on-Chip (NoC) security vulnerabilities and state-of-the-art countermeasures, with contributions from System-on-Chip (SoC) designers, academic researchers and hardware security experts. Readers will gain a clear understanding of the existing security solutions for on-chip communication architectures and how they can be utilized effectively to design secure and trustworthy systems.
In recent years, there has been a considerable amount of effort, both in industry and academia, focusing on the design, implementation, performance analysis, evaluation and prediction of silicon photonic interconnects for inter- and intra-chip communication, paving the way for the design and dimensioning of the next and future generation of high-performance computing systems. Photonic Interconnects for Computing Systems provides a comprehensive overview of the current state-of-the-art technology and research achievements in employing silicon photonics for interconnection networks and high-performance computing, summarizing main opportunities and some challenges. The majority of the chapters were collected from presentations made at the International Workshop on Optical/Photonic Interconnects for Computing Systems (OPTICS) held over the past two years. The workshop invites internationally recognized speakers on the range of topics relevant to silicon photonics and computing systems. Technical topics discussed in the book include:Design and Implementation of Chip-Scale Photonic Interconnects;Developing Design Automation Solutions for Chip-Scale Photonic Interconnects;Design Space Exploration in Chip-Scale Photonic Interconnects;Thermal Analysis and Modeling in Photonic Interconnects;Design for Reliability;Fabrication Non-Uniformity in Photonic Interconnects;Photonic Interconnects for Computing Systems presents a compilation of outstanding contributions from leading research groups in the field. It presents a comprehensive overview of the design, advantages, challenges, and requirements of photonic interconnects for computing systems. The selected contributions present important discussions and approaches related to the design and development of novel photonic interconnect architectures, as well as various design solutions to improve the performance of such systems while considering different challenges. The book is ideal for personnel in computer/photonic industries as well as academic staff and master/graduate students in computer science and engineering, electronic engineering, electrical engineering and photonics.
This book presents recent advances towards the goal of enabling efficient implementation of machine learning models on resource-constrained systems, covering different application domains. The focus is on presenting interesting and new use cases of applying machine learning to innovative application domains, exploring the efficient hardware design of efficient machine learning accelerators, memory optimization techniques, illustrating model compression and neural architecture search techniques for energy-efficient and fast execution on resource-constrained hardware platforms, and understanding hardware-software codesign techniques for achieving even greater energy, reliability, and performance benefits. Discusses efficient implementation of machine learning in embedded, CPS, IoT, and edge computing; Offers comprehensive coverage of hardware design, software design, and hardware/software co-design and co-optimization; Describes real applications to demonstrate how embedded, CPS, IoT, and edge applications benefit from machine learning.
Developing countries are persistently looking for efficient and cost-effective methods for transforming their communities into smart cities. Unfortunately, energy crises have increased in these regions due to a lack of awareness and proper utilization of technological methods. These communities must explore and implement innovative solutions in order to enhance citizen enrollment, quality of government, and city intelligence. IoT Architectures, Models, and Platforms for Smart City Applications provides emerging research exploring the theoretical and practical aspects of transforming cities into intelligent systems using IoT-based design models and sustainable development projects. This publication looks at how cities can be built as smart cities within limited resources and existing advanced technologies. Featuring coverage on a broad range of topics such as cloud computing, human machine interface, and ad hoc networks, this book is ideally designed for urban planners, engineers, IT specialists, computer engineering students, research scientists, academicians, technology developers, policymakers, researchers, and designers seeking current research on smart applications within urban development.
This book focuses on neuromorphic computing principles and organization and how to build fault-tolerant scalable hardware for large and medium scale spiking neural networks with learning capabilities. In addition, the book describes in a comprehensive way the organization and how to design a spike-based neuromorphic system to perform network of spiking neurons communication, computing, and adaptive learning for emerging AI applications. The book begins with an overview of neuromorphic computing systems and explores the fundamental concepts of artificial neural networks. Next, we discuss artificial neurons and how they have evolved in their representation of biological neuronal dynamics. Afterward, we discuss implementing these neural networks in neuron models, storage technologies, inter-neuron communication networks, learning, and various design approaches. Then, comes the fundamental design principle to build an efficient neuromorphic system in hardware. The challenges that need to be solved toward building a spiking neural network architecture with many synapses are discussed. Learning in neuromorphic computing systems and the major emerging memory technologies that promise neuromorphic computing are then given. A particular chapter of this book is dedicated to the circuits and architectures used for communication in neuromorphic systems. In particular, the Network-on-Chip fabric is introduced for receiving and transmitting spikes following the Address Event Representation (AER) protocol and the memory accessing method. In addition, the interconnect design principle is covered to help understand the overall concept of on-chip and off-chip communication. Advanced on-chip interconnect technologies, including si-photonic three-dimensional interconnects and fault-tolerant routing algorithms, are also given. The book also covers the main threats of reliability and discusses several recovery methods for multicore neuromorphic systems. This is important for reliable processing in several embedded neuromorphic applications. A reconfigurable design approach that supports multiple target applications via dynamic reconfigurability, network topology independence, and network expandability is also described in the subsequent chapters. The book ends with a case study about a real hardware-software design of a reliable three-dimensional digital neuromorphic processor geared explicitly toward the 3D-ICs biological brain’s three-dimensional structure. The platform enables high integration density and slight spike delay of spiking networks and features a scalable design. We present methods for fault detection and recovery in a neuromorphic system as well. Neuromorphic Computing Principles and Organization is an excellent resource for researchers, scientists, graduate students, and hardware-software engineers dealing with the ever-increasing demands on fault-tolerance, scalability, and low power consumption. It is also an excellent resource for teaching advanced undergraduate and graduate students about the fundamentals concepts, organization, and actual hardware-software design of reliable neuromorphic systems with learning and fault-tolerance capabilities.
Addresses the Challenges Associated with System-on-Chip Integration Network-on-Chip: The Next Generation of System-on-Chip Integration examines the current issues restricting chip-on-chip communication efficiency, and explores Network-on-chip (NoC), a promising alternative that equips designers with the capability to produce a scalable, reusable, and high-performance communication backbone by allowing for the integration of a large number of cores on a single system-on-chip (SoC). This book provides a basic overview of topics associated with NoC-based design: communication infrastructure design, communication methodology, evaluation framework, and mapping of applications onto NoC. It details the design and evaluation of different proposed NoC structures, low-power techniques, signal integrity and reliability issues, application mapping, testing, and future trends. Utilizing examples of chips that have been implemented in industry and academia, this text presents the full architectural design of components verified through implementation in industrial CAD tools. It describes NoC research and developments, incorporates theoretical proofs strengthening the analysis procedures, and includes algorithms used in NoC design and synthesis. In addition, it considers other upcoming NoC issues, such as low-power NoC design, signal integrity issues, NoC testing, reconfiguration, synthesis, and 3-D NoC design. This text comprises 12 chapters and covers: The evolution of NoC from SoC—its research and developmental challenges NoC protocols, elaborating flow control, available network topologies, routing mechanisms, fault tolerance, quality-of-service support, and the design of network interfaces The router design strategies followed in NoCs The evaluation mechanism of NoC architectures The application mapping strategies followed in NoCs Low-power design techniques specifically followed in NoCs The signal integrity and reliability issues of NoC The details of NoC testing strategies reported so far The problem of synthesizing application-specific NoCs Reconfigurable NoC design issues Direction of future research and development in the field of NoC Network-on-Chip: The Next Generation of System-on-Chip Integration covers the basic topics, technology, and future trends relevant to NoC-based design, and can be used by engineers, students, and researchers and other industry professionals interested in computer architecture, embedded systems, and parallel/distributed systems.
Recently the world celebrated the 60th anniversary of the invention of the first transistor. The first integrated circuit (IC) was built a decade later, with the first microprocessor designed in the early 1970s. Today, ICs are a part of nearly every aspect of our daily lives. They help us live longer and more comfortably, and do more, faster. All this is possible because of the relentless search for new materials, circuit designs, and ideas happening on a daily basis at industrial and academic institutions around the globe. Showcasing the latest advances in very-large-scale integrated (VLSI) circuits, VLSI: Circuits for Emerging Applications provides a balanced view of industrial and academic developments beyond silicon and complementary metal–oxide–semiconductor (CMOS) technology. From quantum-dot cellular automata (QCA) to chips for cochlear implants, this must-have resource: Investigates the trend of combining multiple cores in a single chip to boost performance of the overall system Describes a novel approach to enable physically unclonable functions (PUFs) using intrinsic features of a VLSI chip Examines the VLSI implementations of major symmetric and asymmetric key cryptographic algorithms, hash functions, and digital signatures Discusses nonvolatile memories such as resistive random-access memory (Re-RAM), magneto-resistive RAM (MRAM), and floating-body RAM (FB-RAM) Explores organic transistors, soft errors, photonics, nanoelectromechanical (NEM) relays, reversible computation, bioinformatics, asynchronous logic, and more VLSI: Circuits for Emerging Applications presents cutting-edge research, design architectures, materials, and uses for VLSI circuits, offering valuable insight into the current state of the art of micro- and nanoelectronics.
This book sets out to build bridges between the domains of photonic device physics and neural networks, providing a comprehensive overview of the emerging field of "neuromorphic photonics." It includes a thorough discussion of evolution of neuromorphic photonics from the advent of fiber-optic neurons to today’s state-of-the-art integrated laser neurons, which are a current focus of international research. Neuromorphic Photonics explores candidate interconnection architectures and devices for integrated neuromorphic networks, along with key functionality such as learning. It is written at a level accessible to graduate students, while also intending to serve as a comprehensive reference for experts in the field.
